Usually refers to the use of mathematical models in the prediction of learning to perform tasks based on the theory of probability applied to responses; it may also refer to the frequency of occurrence of the responses observed in the particular study.
The study of chance processes or the relative frequency characterizing a chance process.
Learning the correct route through a maze to obtain reinforcement. It is used for human or animal populations. (Thesaurus of Psychological Index Terms, 6th ed)
Instructional use of examples or cases to teach using problem-solving skills and critical thinking.
Learning that is manifested in the ability to respond differentially to various stimuli.
A response to a cue that is instrumental in avoiding a noxious experience.
Learning to respond verbally to a verbal stimulus cue.
Any situation where an animal or human is trained to respond differentially to two stimuli (e.g., approach and avoidance) under reward and punishment conditions and subsequently trained under reversed reward values (i.e., the approach which was previously rewarded is punished and vice versa).
Learning to make a series of responses in exact order.
Complex mental function having four distinct phases: (1) memorizing or learning, (2) retention, (3) recall, and (4) recognition. Clinically, it is usually subdivided into immediate, recent, and remote memory.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.
Learning that takes place when a conditioned stimulus is paired with an unconditioned stimulus.
The educational process of instructing.
The branch of mathematics dealing with the purely logical properties of probability. Its theorems underlie most statistical methods. (Last, A Dictionary of Epidemiology, 2d ed)
The capacity of the NERVOUS SYSTEM to change its reactivity as the result of successive activations.
The observable response an animal makes to any situation.
The coordination of a sensory or ideational (cognitive) process and a motor activity.
Performance of complex motor acts.
A general term referring to the learning of some particular response.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
A curved elevation of GRAY MATTER extending the entire length of the floor of the TEMPORAL HORN of the LATERAL VENTRICLE (see also TEMPORAL LOBE). The hippocampus proper, subiculum, and DENTATE GYRUS constitute the hippocampal formation. Sometimes authors include the ENTORHINAL CORTEX in the hippocampal formation.
The assessing of academic or educational achievement. It includes all aspects of testing and test construction.
Change in learning in one situation due to prior learning in another situation. The transfer can be positive (with second learning improved by first) or negative (where the reverse holds).
The persistence to perform a learned behavior (facts or experiences) after an interval has elapsed in which there has been no performance or practice of the behavior.
Elements of limited time intervals, contributing to particular results or situations.
The time from the onset of a stimulus until a response is observed.
Computer-based representation of physical systems and phenomena such as chemical processes.
A self-learning technique, usually online, involving interaction of the student with programmed instructional materials.
A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
A tree of the family Sterculiaceae (or Byttneriaceae), usually Theobroma cacao, or its seeds, which after fermentation and roasting, yield cocoa and chocolate.
Sweet food products combining cane or beet sugars with other carbohydrates and chocolate, milk, eggs, and various flavorings. In the United States, candy refers to both sugar- and cocoa-based confections and is differentiated from sweetened baked goods; elsewhere the terms sugar confectionary, chocolate confectionary, and flour confectionary (meaning goods such as cakes and pastries) are used.
The study of natural phenomena by observation, measurement, and experimentation.
Disciplines concerned with the interrelationships of individuals in a social environment including social organizations and institutions. Includes Sociology and Anthropology.
Those individuals engaged in research.

Behavioral and pharmacological variables affecting risky choice in rats. (1/169)

The effects of manipulations of response requirement, intertrial interval (ITI), and psychoactive drugs (ethanol, phencyclidine, and d-amphetamine) on lever choice under concurrent fixed-ratio schedules were investigated in rats. Responding on the "certain'' lever produced three 45-mg pellets, whereas responding on the "risky" lever produced either 15 pellets (p = .33) or no pellets (p .67). Rats earned all food during the session, which ended after 12 forced trials and 93 choice trials or 90 min, whichever occurred first. When the response requirement was increased from 1 to 16 and the ITI was 20 s, percentage of risky choice was inversely related to fixed-ratio value. When only a single response was required but the ITI was manipulated between 20 and 120 s (with maximum session duration held constant), percentage of risky choice was directly related to length of the ITI. The effects of the drugs were investigated first at an ITI of 20 s, when risky choice was low for most rats, and then at an ITI of 80 s, when risky choice was higher for most rats. Ethanol usually decreased risky choice. Phencyclidine did not usually affect risky choice when the ITI was 20 s but decreased it in half the rats when the ITI was 80 s. For d-amphetamine, the effects appeared to he related to baseline probability of risky choice; that is, low probabilities were increased and high probabilities were decreased. Although increase in risky choice as a function of the ITI is at variance with previous ITI data, it is consistent with foraging data showing that risk aversion decreases as food availability decreases. The pharmacological manipulations showed that drug effects on risky choice may be influenced by the baseline probability of risky choice, just as drug effects can be a function of baseline response rate.  (+info)

High-probability stimulus control topographies with delayed S+ onset in a simultaneous discrimination procedure. (2/169)

Experimenters and teachers use discrimination learning procedures to encourage reliable attending to stimulus differences defined as relevant for their purposes. Put another way, the goal of discrimination training is to establish high-probability stimulus control topographies that are coherent with experimenter or teacher specifications. The present research was conducted to investigate a novel procedure for encouraging stimulus control topography coherence. Participants were 13 adolescents with severe intellectual handicaps. During an initial Condition A, all were exposed to a simultaneous discrimination procedure. Participants could select a form alternating with a black field (S+) or an identical form that did not alternate (S-). Accuracy scores were typically low, and there was little evidence of coherent stimulus control topographies. Subsequently, the procedure was changed. During Condition B, every trial initially presented two identical nonalternating S- forms (Trial State 1). If the participant made no selection for 5 s, one of the forms began to alternate with the black field, and he or she could make the S+/S- discrimination (Trial State 2). Selections during Trial State I prolonged the delay to Trial State 2 until there had been no response for 5 s. During Condition B, S+/S- discrimination accuracy scores improved rapidly and markedly for most participants. Reinstating Condition A often resulted in diminished accuracy scores. This study thus (a) demonstrated a novel procedure for encouraging stimulus control topography coherence and (b) provided support for the interpretation that intermediate accuracy scores may be due to different topographies of stimulus control that co-occur in the same discriminative baseline.  (+info)

Defining the neural mechanisms of probabilistic reversal learning using event-related functional magnetic resonance imaging. (3/169)

Event-related functional magnetic resonance imaging was used to measure blood oxygenation level-dependent responses in 13 young healthy human volunteers during performance of a probabilistic reversal-learning task. The task allowed the separate investigation of the relearning of stimulus-reward associations and the reception of negative feedback. Significant signal change in the right ventrolateral prefrontal cortex was demonstrated on trials when subjects stopped responding to the previously relevant stimulus and shifted responding to the newly relevant stimulus. Significant signal change in the region of the ventral striatum was also observed on such reversal errors, from a region of interest analysis. The ventrolateral prefrontal cortex and ventral striatum were not significantly activated by the other, preceding reversal errors, or when subjects received negative feedback for correct responses. Moreover, the response on the final reversal error, before shifting, was not modulated by the number of preceding reversal errors, indicating that error-related activity does not simply accumulate in this network. The signal change in this ventral frontostriatal circuit is therefore associated with reversal learning and is uncontaminated by negative feedback. Overall, these data concur with findings in rodents and nonhuman primates of reversal-learning deficits after damage to ventral frontostriatal circuitry, and also support recent clinical findings using this task.  (+info)

The development of emergent differential sample behavior in pigeons. (4/169)

Three experiments attempted to replicate Manabe, Kawashima, and Staddon's (1995) finding of emergent differential sample behavior in budgerigars that has been interpreted as evidence of functional equivalence class formation. In Experiments 1 and 2, pigeons initially learned two-sample/ two-alternative matching to sample in which comparison presentation was contingent on pecking one sample on a differential-reinforcement-of-low-rate (DRL) schedule and the other on a fixed-ratio (FR) schedule. Later, two new samples were added to the task. Comparison presentation on these trials occurred after the first sample peck following a predetermined interval (Experiment 1) or after completion of either the DRL or FR requirement, whichever occurred first (Experiment 2). Experiment 1 found no evidence for emergent spaced versus rapid responding to the new samples as they established conditional control over the familiar choices. By contrast, differential responding did emerge for some pigeons in Experiment 2, with responding to each new sample coinciding with the pattern explicitly conditioned to the original sample occasioning the same comparison choice. This emergent effect, however, disappeared for most pigeons with continued training. Experiment 3 systematically replicated Experiment 2 using differential peck location as the sample behavior. Differential location pecking emerged to the new samples for most pigeons and remained intact throughout training. Our findings demonstrate a viable pigeon analogue to the budgerigar emergent calling paradigm and are discussed in terms of equivalence- and non-equivalence-based processes.  (+info)

Contextual control of stimulus generalization and stimulus equivalence in hierarchical categorization. (5/169)

The purpose of this study was to determine whether hierarchical categorization would result from a combination of contextually controlled conditional discrimination training, stimulus generalization, and stimulus equivalence. First, differential selection responses to a specific stimulus feature were brought under contextual control. This contextual control was hierarchical in that stimuli at the top of the hierarchy all evoked one response, whereas those at the bottom each evoked different responses. The evocative functions of these stimuli generalized in predictable ways along a dimension of physical similarity. Then, these functions were indirectly acquired by a set of nonsense syllables that were related via transitivity relations to the originally trained stimuli. These nonsense syllables effectively served as names for the different stimulus classes within each level of the hierarchy.  (+info)

Toward a unified theory of decision criterion learning in perceptual categorization. (6/169)

Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs are unequal, suboptimal decision criterion placement is observed, even when the optimal decision criterion is identical in both cases. A series of studies are reviewed that examine the generality of this finding, and a unified theory of decision criterion learning is described (Maddox & Dodd, 2001). The theory assumes that two critical mechanisms operate in decision criterion learning. One mechanism involves competition between reward and accuracy maximization: The observer attempts to maximize reward, as instructed, but also places some importance on accuracy maximization. The second mechanism involves a flat-maxima hypothesis that assumes that the observer's estimate of the reward-maximizing decision criterion is determined from the steepness of the objective reward function that relates expected reward to decision criterion placement. Experiments used to develop and test the theory require each observer to complete a large number of trials and to participate in all conditions of the experiment. This provides maximal control over the reinforcement history of the observer and allows a focus on individual behavioral profiles. The theory is applied to decision criterion learning problems that examine category discriminability, payoff matrix multiplication and addition effects, the optimal classifier's independence assumption, and different types of trial-by-trial feedback. In every case the theory provides a good account of the data, and, most important, provides useful insights into the psychological processes involved in decision criterion learning.  (+info)

Analysis of probabilistic classification learning in patients with Parkinson's disease before and after pallidotomy surgery. (7/169)

This study examined the characteristics of probabilistic classification learning, a form of implicit learning previously shown to be impaired in patients with basal ganglia dysfunction (e.g., Parkinson's disease). In this task, subjects learn to predict the weather using associations that are formed gradually across many trials, because of the probabilistic nature of the cue-outcome relationships. Patients with Parkinson's disease, both before and after pallidotomy, and age-matched control subjects, exhibited evidence of probabilistic classification learning across 100 training trials. However, pallidotomy appears to hinder the learning of associations most implicit in nature (i.e., weakly associated cues). Although subjects were most sensitive to single-cue associations when learning the task, there is evidence that cue combinations contribute significantly to probability learning. The utility of multiple dependent measures is discussed.  (+info)

Bootstrapped learning of novel objects. (8/169)

Recognition of familiar objects in cluttered backgrounds is a challenging computational problem. Camouflage provides a particularly striking case, where an object is difficult to detect, recognize, and segment even when in "plain view." Current computational approaches combine low-level features with high-level models to recognize objects. But what if the object is unfamiliar? A novel camouflaged object poses a paradox: A visual system would seem to require a model of an object's shape in order to detect, recognize, and segment it when camouflaged. But, how is the visual system to build such a model of the object without easily segmentable samples? One possibility is that learning to identify and segment is opportunistic in the sense that learning of novel objects takes place only when distinctive clues permit object segmentation from background, such as when target color or motion enables segmentation on single presentations. We tested this idea and discovered that, on the contrary, human observers can learn to identify and segment a novel target shape, even when for any given training image the target object is camouflaged. Further, perfect recognition can be achieved without accurate segmentation. We call the ability to build a shape model from high-ambiguity presentations bootstrapped learning.  (+info)

"Probability learning" is not a widely recognized or used term in medicine. However, it is a concept that may be relevant to the field of behavioral medicine and psychology. In those contexts, probability learning refers to the process by which individuals learn to predict the likelihood or probability of certain events or outcomes based on past experiences or observations.

In medical research, the term "probability" is often used to describe the likelihood that a particular event will occur, such as the probability of developing a disease given exposure to a certain risk factor. This concept is central to the field of epidemiology and biostatistics, where researchers use statistical methods to estimate the probability of various health outcomes based on large datasets.

However, "probability learning" in the context of medical research typically refers to the process by which individuals learn to make accurate judgments about probabilities based on data or evidence. This may involve learning to recognize patterns in data, using statistical models to estimate probabilities, or applying principles of probability theory to clinical decision-making.

Overall, while "probability learning" is not a formal medical term, it is a concept that has relevance to various areas of medicine, including behavioral medicine, epidemiology, and biostatistics.

In the context of medicine and healthcare, 'probability' does not have a specific medical definition. However, in general terms, probability is a branch of mathematics that deals with the study of numerical quantities called probabilities, which are assigned to events or sets of events. Probability is a measure of the likelihood that an event will occur. It is usually expressed as a number between 0 and 1, where 0 indicates that the event is impossible and 1 indicates that the event is certain to occur.

In medical research and statistics, probability is often used to quantify the uncertainty associated with statistical estimates or hypotheses. For example, a p-value is a probability that measures the strength of evidence against a hypothesis. A small p-value (typically less than 0.05) suggests that the observed data are unlikely under the assumption of the null hypothesis, and therefore provides evidence in favor of an alternative hypothesis.

Probability theory is also used to model complex systems and processes in medicine, such as disease transmission dynamics or the effectiveness of medical interventions. By quantifying the uncertainty associated with these models, researchers can make more informed decisions about healthcare policies and practices.

Maze learning is not a medical term per se, but it is a concept that is often used in the field of neuroscience and psychology. It refers to the process by which an animal or human learns to navigate through a complex environment, such as a maze, in order to find its way to a goal or target.

Maze learning involves several cognitive processes, including spatial memory, learning, and problem-solving. As animals or humans navigate through the maze, they encode information about the location of the goal and the various landmarks within the environment. This information is then used to form a cognitive map that allows them to navigate more efficiently in subsequent trials.

Maze learning has been widely used as a tool for studying learning and memory processes in both animals and humans. For example, researchers may use maze learning tasks to investigate the effects of brain damage or disease on cognitive function, or to evaluate the efficacy of various drugs or interventions for improving cognitive performance.

Problem-Based Learning (PBL) is not a medical term per se, but rather a teaching and learning approach that has been widely adopted in medical education. Here's a definition of PBL from the medical education perspective:

Problem-Based Learning is an educational method that utilizes clinical cases or real-world problems as a starting point for students to learn and apply concepts and principles from various disciplines. In this approach, students work in small groups to identify learning needs, gather relevant information, analyze and synthesize data, formulate hypotheses, develop solutions, and reflect on their learning process. The role of the instructor is that of a facilitator who guides the learners in their exploration of the problem and provides feedback on their performance. PBL aims to promote critical thinking, self-directed learning, collaborative skills, and clinical reasoning among medical students.

Discrimination learning is a type of learning in which an individual learns to distinguish between two or more stimuli and respond differently to each. It involves the ability to recognize the differences between similar stimuli and to respond appropriately based on the specific characteristics of each stimulus. This type of learning is important for many aspects of cognition, including perception, language, and problem-solving.

In discrimination learning, an individual may be presented with two or more stimuli and reinforced for responding differently to each. For example, a person might be trained to press a button in response to the color red and to do nothing in response to the color green. Through this process of differential reinforcement, the individual learns to discriminate between the two colors and to respond appropriately to each.

Discrimination learning is often studied in animals as well as humans, and it is thought to involve a range of cognitive processes, including attention, memory, and perception. It is an important aspect of many forms of learning and plays a role in a wide variety of behaviors.

Avoidance learning is a type of conditioning in which an individual learns to act in a certain way to avoid experiencing an unpleasant or aversive stimulus. It is a form of learning that occurs when an organism changes its behavior to avoid a negative outcome or situation. This can be seen in both animals and humans, and it is often studied in the field of psychology and neuroscience.

In avoidance learning, the individual learns to associate a particular cue or stimulus with the unpleasant experience. Over time, they learn to perform an action to escape or avoid the cue, thereby preventing the negative outcome from occurring. For example, if a rat receives an electric shock every time it hears a certain tone, it may eventually learn to press a lever to turn off the tone and avoid the shock.

Avoidance learning can be adaptive in some situations, as it allows individuals to avoid dangerous or harmful stimuli. However, it can also become maladaptive if it leads to excessive fear or anxiety, or if it interferes with an individual's ability to function in daily life. For example, a person who has been attacked may develop a phobia of public places and avoid them altogether, even though this limits their ability to engage in social activities and live a normal life.

In summary, avoidance learning is a type of conditioning in which an individual learns to act in a certain way to avoid experiencing an unpleasant or aversive stimulus. It can be adaptive in some situations but can also become maladaptive if it leads to excessive fear or anxiety or interferes with daily functioning.

Verbal learning is a type of learning that involves the acquisition, processing, and retrieval of information presented in a verbal or written form. It is often assessed through tasks such as list learning, where an individual is asked to remember a list of words or sentences after a single presentation or multiple repetitions. Verbal learning is an important aspect of cognitive functioning and is commonly evaluated in neuropsychological assessments to help identify any memory or learning impairments.

Reversal learning is a neuropsychological concept that refers to the ability to adjust behavioral responses when reward contingencies are changed or reversed. In other words, it is the capacity to learn and adapt to new rules when the previous ones no longer apply or are no longer reinforced. This cognitive process is often studied in animal models and human subjects using various learning paradigms, such as classical or operant conditioning tasks.

In a typical reversal learning task, a subject is initially trained to associate a particular stimulus (e.g., visual cue, sound, or action) with a reward (e.g., food or water). Once the subject has learned this association and responds consistently to the stimulus, the reinforcement contingency is reversed, so that the previously reinforced stimulus is now unreinforced, and the previously unreinforced stimulus is now reinforced. The subject must then learn and adapt to this new reward contingency.

Reversal learning involves several cognitive processes, including attention, memory, motivation, and executive functions. It requires the ability to inhibit a previously learned response, update working memory with new information, and flexibly adjust behavior based on changing environmental demands. Deficits in reversal learning have been observed in various neurological and psychiatric conditions, such as Parkinson's disease, Huntington's disease, schizophrenia, and substance use disorders, suggesting that this cognitive process may be a useful marker of brain dysfunction in these conditions.

Serial learning is a form of learning in which new information or skills are acquired and organized in a sequential manner, with each piece of information building on the previous one. In other words, it involves learning items or concepts one at a time, in a specific order, rather than all at once. This type of learning is often used in situations where the material to be learned has a clear sequence, such as learning the alphabet, numbers, or days of the week.

In a medical context, serial learning may be used to teach complex medical procedures or concepts that have multiple steps or components. For example, a medical student may learn how to perform a physical examination by first learning how to take a patient's vital signs, then moving on to inspecting various parts of the body in a specific order. Through repeated practice and reinforcement, the student gradually builds up a sequence of skills and knowledge that becomes integrated into their long-term memory.

It is worth noting that some individuals may find serial learning more challenging than other forms of learning, particularly if they have difficulty with sequential processing or working memory limitations. Therefore, individualized instruction and accommodations may be necessary to support learners who struggle with serial learning tasks.

In the context of medical and clinical neuroscience, memory is defined as the brain's ability to encode, store, retain, and recall information or experiences. Memory is a complex cognitive process that involves several interconnected regions of the brain and can be categorized into different types based on various factors such as duration and the nature of the information being remembered.

The major types of memory include:

1. Sensory memory: The shortest form of memory, responsible for holding incoming sensory information for a brief period (less than a second to several seconds) before it is either transferred to short-term memory or discarded.
2. Short-term memory (also called working memory): A temporary storage system that allows the brain to hold and manipulate information for approximately 20-30 seconds, although this duration can be extended through rehearsal strategies. Short-term memory has a limited capacity, typically thought to be around 7±2 items.
3. Long-term memory: The memory system responsible for storing large amounts of information over extended periods, ranging from minutes to a lifetime. Long-term memory has a much larger capacity compared to short-term memory and is divided into two main categories: explicit (declarative) memory and implicit (non-declarative) memory.

Explicit (declarative) memory can be further divided into episodic memory, which involves the recollection of specific events or episodes, including their temporal and spatial contexts, and semantic memory, which refers to the storage and retrieval of general knowledge, facts, concepts, and vocabulary, independent of personal experience or context.

Implicit (non-declarative) memory encompasses various forms of learning that do not require conscious awareness or intention, such as procedural memory (skills and habits), priming (facilitated processing of related stimuli), classical conditioning (associative learning), and habituation (reduced responsiveness to repeated stimuli).

Memory is a crucial aspect of human cognition and plays a significant role in various aspects of daily life, including learning, problem-solving, decision-making, social interactions, and personal identity. Memory dysfunction can result from various neurological and psychiatric conditions, such as dementia, Alzheimer's disease, stroke, traumatic brain injury, and depression.

An algorithm is not a medical term, but rather a concept from computer science and mathematics. In the context of medicine, algorithms are often used to describe step-by-step procedures for diagnosing or managing medical conditions. These procedures typically involve a series of rules or decision points that help healthcare professionals make informed decisions about patient care.

For example, an algorithm for diagnosing a particular type of heart disease might involve taking a patient's medical history, performing a physical exam, ordering certain diagnostic tests, and interpreting the results in a specific way. By following this algorithm, healthcare professionals can ensure that they are using a consistent and evidence-based approach to making a diagnosis.

Algorithms can also be used to guide treatment decisions. For instance, an algorithm for managing diabetes might involve setting target blood sugar levels, recommending certain medications or lifestyle changes based on the patient's individual needs, and monitoring the patient's response to treatment over time.

Overall, algorithms are valuable tools in medicine because they help standardize clinical decision-making and ensure that patients receive high-quality care based on the latest scientific evidence.

Artificial Intelligence (AI) in the medical context refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.

In healthcare, AI is increasingly being used to analyze large amounts of data, identify patterns, make decisions, and perform tasks that would normally require human intelligence. This can include tasks such as diagnosing diseases, recommending treatments, personalizing patient care, and improving clinical workflows.

Examples of AI in medicine include machine learning algorithms that analyze medical images to detect signs of disease, natural language processing tools that extract relevant information from electronic health records, and robot-assisted surgery systems that enable more precise and minimally invasive procedures.

Classical conditioning is a type of learning process that occurs when two stimuli are repeatedly paired together, leading to an association between them. This concept was first introduced by Ivan Pavlov, a Russian physiologist, in his studies on classical conditioning in the late 19th and early 20th centuries.

In classical conditioning, there are typically two types of stimuli involved: the unconditioned stimulus (US) and the neutral stimulus (NS). The US is a stimulus that naturally triggers a response, known as the unconditioned response (UR), in an organism. For example, food is an US that triggers salivation, which is the UR, in dogs.

The NS, on the other hand, is a stimulus that does not initially trigger any response in the organism. However, when the NS is repeatedly paired with the US, it becomes a conditioned stimulus (CS) and begins to elicit a conditioned response (CR). The CR is similar to the UR but is triggered by the CS instead of the US.

For example, if Pavlov repeatedly rang a bell (NS) just before presenting food (US) to a dog, the dog would eventually start salivating (CR) in response to the bell (CS) even when food was not presented. This is an example of classical conditioning.

Classical conditioning has been widely studied and is believed to play a role in various physiological processes, such as learning, memory, and emotion regulation. It has also been used in various applications, including behavioral therapy and advertising.

I'm sorry for any confusion, but "teaching" is a general term that refers to the act of imparting knowledge or skills to others and is not a medical term. However, in the context of medical education, teaching refers to the instruction and training of medical students, residents, fellows, and other healthcare professionals to develop their knowledge, skills, and attitudes necessary for providing high-quality patient care. This can take place in various settings such as classrooms, clinical wards, simulation centers, or online platforms, and can involve a range of teaching methods including lectures, small group discussions, bedside teaching, case-based learning, and hands-on training.

I'm sorry for any confusion, but "Probability Theory" is actually a branch of mathematics, not medicine. It provides a formal framework for quantifying and reasoning about uncertainty. It involves concepts such as random variables, probability distributions, expected values, and statistical inferences. While it is widely used in many scientific fields, including medical research, it is not a medical term itself.

Neuronal plasticity, also known as neuroplasticity or neural plasticity, refers to the ability of the brain and nervous system to change and adapt as a result of experience, learning, injury, or disease. This can involve changes in the structure, organization, and function of neurons (nerve cells) and their connections (synapses) in the central and peripheral nervous systems.

Neuronal plasticity can take many forms, including:

* Synaptic plasticity: Changes in the strength or efficiency of synaptic connections between neurons. This can involve the formation, elimination, or modification of synapses.
* Neural circuit plasticity: Changes in the organization and connectivity of neural circuits, which are networks of interconnected neurons that process information.
* Structural plasticity: Changes in the physical structure of neurons, such as the growth or retraction of dendrites (branches that receive input from other neurons) or axons (projections that transmit signals to other neurons).
* Functional plasticity: Changes in the physiological properties of neurons, such as their excitability, responsiveness, or sensitivity to stimuli.

Neuronal plasticity is a fundamental property of the nervous system and plays a crucial role in many aspects of brain function, including learning, memory, perception, and cognition. It also contributes to the brain's ability to recover from injury or disease, such as stroke or traumatic brain injury.

'Animal behavior' refers to the actions or responses of animals to various stimuli, including their interactions with the environment and other individuals. It is the study of the actions of animals, whether they are instinctual, learned, or a combination of both. Animal behavior includes communication, mating, foraging, predator avoidance, and social organization, among other things. The scientific study of animal behavior is called ethology. This field seeks to understand the evolutionary basis for behaviors as well as their physiological and psychological mechanisms.

Psychomotor performance refers to the integration and coordination of mental processes (cognitive functions) with physical movements. It involves the ability to perform complex tasks that require both cognitive skills, such as thinking, remembering, and perceiving, and motor skills, such as gross and fine motor movements. Examples of psychomotor performances include driving a car, playing a musical instrument, or performing surgical procedures.

In a medical context, psychomotor performance is often used to assess an individual's ability to perform activities of daily living (ADLs) and instrumental activities of daily living (IADLs), such as bathing, dressing, cooking, cleaning, and managing medications. Deficits in psychomotor performance can be a sign of neurological or psychiatric disorders, such as dementia, Parkinson's disease, or depression.

Assessment of psychomotor performance may involve tests that measure reaction time, coordination, speed, precision, and accuracy of movements, as well as cognitive functions such as attention, memory, and problem-solving skills. These assessments can help healthcare professionals develop appropriate treatment plans and monitor the progression of diseases or the effectiveness of interventions.

Motor skills are defined as the abilities required to plan, control and execute physical movements. They involve a complex interplay between the brain, nerves, muscles, and the environment. Motor skills can be broadly categorized into two types: fine motor skills, which involve small, precise movements (such as writing or picking up small objects), and gross motor skills, which involve larger movements using the arms, legs, and torso (such as crawling, walking, or running).

Motor skills development is an essential aspect of child growth and development, and it continues to evolve throughout adulthood. Difficulties with motor skills can impact a person's ability to perform daily activities and can be associated with various neurological and musculoskeletal conditions.

Statistical models are mathematical representations that describe the relationship between variables in a given dataset. They are used to analyze and interpret data in order to make predictions or test hypotheses about a population. In the context of medicine, statistical models can be used for various purposes such as:

1. Disease risk prediction: By analyzing demographic, clinical, and genetic data using statistical models, researchers can identify factors that contribute to an individual's risk of developing certain diseases. This information can then be used to develop personalized prevention strategies or early detection methods.

2. Clinical trial design and analysis: Statistical models are essential tools for designing and analyzing clinical trials. They help determine sample size, allocate participants to treatment groups, and assess the effectiveness and safety of interventions.

3. Epidemiological studies: Researchers use statistical models to investigate the distribution and determinants of health-related events in populations. This includes studying patterns of disease transmission, evaluating public health interventions, and estimating the burden of diseases.

4. Health services research: Statistical models are employed to analyze healthcare utilization, costs, and outcomes. This helps inform decisions about resource allocation, policy development, and quality improvement initiatives.

5. Biostatistics and bioinformatics: In these fields, statistical models are used to analyze large-scale molecular data (e.g., genomics, proteomics) to understand biological processes and identify potential therapeutic targets.

In summary, statistical models in medicine provide a framework for understanding complex relationships between variables and making informed decisions based on data-driven insights.

The hippocampus is a complex, curved formation in the brain that resembles a seahorse (hence its name, from the Greek word "hippos" meaning horse and "kampos" meaning sea monster). It's part of the limbic system and plays crucial roles in the formation of memories, particularly long-term ones.

This region is involved in spatial navigation and cognitive maps, allowing us to recognize locations and remember how to get to them. Additionally, it's one of the first areas affected by Alzheimer's disease, which often results in memory loss as an early symptom.

Anatomically, it consists of two main parts: the Ammon's horn (or cornu ammonis) and the dentate gyrus. These structures are made up of distinct types of neurons that contribute to different aspects of learning and memory.

Educational measurement is a field of study concerned with the development, administration, and interpretation of tests, questionnaires, and other assessments for the purpose of measuring learning outcomes, abilities, knowledge, skills, and attitudes in an educational context. The goal of educational measurement is to provide valid, reliable, and fair measures of student achievement and growth that can inform instructional decisions, guide curriculum development, and support accountability efforts.

Educational measurement involves a variety of statistical and psychometric methods for analyzing assessment data, including classical test theory, item response theory, and generalizability theory. These methods are used to establish the reliability and validity of assessments, as well as to score and interpret student performance. Additionally, educational measurement is concerned with issues related to test fairness, accessibility, and bias, and seeks to ensure that assessments are equitable and inclusive for all students.

Overall, educational measurement plays a critical role in ensuring the quality and effectiveness of educational programs and policies, and helps to promote student learning and achievement.

In the field of medicine, "time factors" refer to the duration of symptoms or time elapsed since the onset of a medical condition, which can have significant implications for diagnosis and treatment. Understanding time factors is crucial in determining the progression of a disease, evaluating the effectiveness of treatments, and making critical decisions regarding patient care.

For example, in stroke management, "time is brain," meaning that rapid intervention within a specific time frame (usually within 4.5 hours) is essential to administering tissue plasminogen activator (tPA), a clot-busting drug that can minimize brain damage and improve patient outcomes. Similarly, in trauma care, the "golden hour" concept emphasizes the importance of providing definitive care within the first 60 minutes after injury to increase survival rates and reduce morbidity.

Time factors also play a role in monitoring the progression of chronic conditions like diabetes or heart disease, where regular follow-ups and assessments help determine appropriate treatment adjustments and prevent complications. In infectious diseases, time factors are crucial for initiating antibiotic therapy and identifying potential outbreaks to control their spread.

Overall, "time factors" encompass the significance of recognizing and acting promptly in various medical scenarios to optimize patient outcomes and provide effective care.

Reaction time, in the context of medicine and physiology, refers to the time period between the presentation of a stimulus and the subsequent initiation of a response. This complex process involves the central nervous system, particularly the brain, which perceives the stimulus, processes it, and then sends signals to the appropriate muscles or glands to react.

There are different types of reaction times, including simple reaction time (responding to a single, expected stimulus) and choice reaction time (choosing an appropriate response from multiple possibilities). These measures can be used in clinical settings to assess various aspects of neurological function, such as cognitive processing speed, motor control, and alertness.

However, it is important to note that reaction times can be influenced by several factors, including age, fatigue, attention, and the use of certain medications or substances.

A computer simulation is a process that involves creating a model of a real-world system or phenomenon on a computer and then using that model to run experiments and make predictions about how the system will behave under different conditions. In the medical field, computer simulations are used for a variety of purposes, including:

1. Training and education: Computer simulations can be used to create realistic virtual environments where medical students and professionals can practice their skills and learn new procedures without risk to actual patients. For example, surgeons may use simulation software to practice complex surgical techniques before performing them on real patients.
2. Research and development: Computer simulations can help medical researchers study the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone. By creating detailed models of cells, tissues, organs, or even entire organisms, researchers can use simulation software to explore how these systems function and how they respond to different stimuli.
3. Drug discovery and development: Computer simulations are an essential tool in modern drug discovery and development. By modeling the behavior of drugs at a molecular level, researchers can predict how they will interact with their targets in the body and identify potential side effects or toxicities. This information can help guide the design of new drugs and reduce the need for expensive and time-consuming clinical trials.
4. Personalized medicine: Computer simulations can be used to create personalized models of individual patients based on their unique genetic, physiological, and environmental characteristics. These models can then be used to predict how a patient will respond to different treatments and identify the most effective therapy for their specific condition.

Overall, computer simulations are a powerful tool in modern medicine, enabling researchers and clinicians to study complex systems and make predictions about how they will behave under a wide range of conditions. By providing insights into the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone, computer simulations are helping to advance our understanding of human health and disease.

Computer-Assisted Instruction (CAI) is a type of educational technology that involves the use of computers to deliver, support, and enhance learning experiences. In a medical context, CAI can be used to teach a variety of topics, including anatomy, physiology, pharmacology, and clinical skills.

CAI typically involves interactive multimedia presentations, simulations, quizzes, and other activities that engage learners and provide feedback on their performance. It may also include adaptive learning systems that adjust the content and pace of instruction based on the learner's abilities and progress.

CAI has been shown to be effective in improving knowledge retention, critical thinking skills, and learner satisfaction in medical education. It can be used as a standalone teaching method or in combination with traditional classroom instruction or clinical experiences.

Bayes' theorem, also known as Bayes' rule or Bayes' formula, is a fundamental principle in the field of statistics and probability theory. It describes how to update the probability of a hypothesis based on new evidence or data. The theorem is named after Reverend Thomas Bayes, who first formulated it in the 18th century.

In mathematical terms, Bayes' theorem states that the posterior probability of a hypothesis (H) given some observed evidence (E) is proportional to the product of the prior probability of the hypothesis (P(H)) and the likelihood of observing the evidence given the hypothesis (P(E|H)):

Posterior Probability = P(H|E) = [P(E|H) x P(H)] / P(E)

Where:

* P(H|E): The posterior probability of the hypothesis H after observing evidence E. This is the probability we want to calculate.
* P(E|H): The likelihood of observing evidence E given that the hypothesis H is true.
* P(H): The prior probability of the hypothesis H before observing any evidence.
* P(E): The marginal likelihood or probability of observing evidence E, regardless of whether the hypothesis H is true or not. This value can be calculated as the sum of the products of the likelihood and prior probability for all possible hypotheses: P(E) = Σ[P(E|Hi) x P(Hi)]

Bayes' theorem has many applications in various fields, including medicine, where it can be used to update the probability of a disease diagnosis based on test results or other clinical findings. It is also widely used in machine learning and artificial intelligence algorithms for probabilistic reasoning and decision making under uncertainty.

The term "cacao" refers to the plant Theobroma cacao, which is native to tropical regions of Central and South America. It is a small evergreen tree that produces large, football-shaped fruits called pods. Each pod contains 20-60 seeds, also known as beans, which are used to make cocoa powder and chocolate.

Cacao beans contain several bioactive compounds, including flavonoids, theobromine, and caffeine, that have been shown to have potential health benefits. However, it is important to note that these benefits are typically associated with moderate consumption of cocoa products, rather than large amounts of chocolate or cacao beans themselves.

In summary, while "cacao" may be used interchangeably with "cocoa" in some contexts, the term technically refers to the plant and its seeds, rather than the processed powder or chocolate that is derived from them.

I believe you are asking for a medical explanation or definition of "candy," which is not a term typically used in the medical field. However, I can provide some context regarding how it may be discussed in a medical setting.

Candy, also known as sweets or confectionery, generally refers to sugary snacks and treats. In a medical context, excessive consumption of candy can contribute to dental caries (cavities), obesity, type 2 diabetes, and other health issues related to poor nutrition and high sugar intake.

Healthcare professionals may advise patients, particularly children, to limit their candy consumption due to these potential health risks. Additionally, candies that contain ingredients like nuts or allergens can pose a risk for individuals with specific food allergies.

I'm sorry for any confusion, but "Science" is a broad field that refers to a systematic and logical process used to discover how things in the universe work. It's not typically used as a medical term. However, within the context of medicine, "science" often refers to evidence-based practices, which are treatments and preventions that have been scientifically researched and proven to be effective. This could include areas like pharmacology (the study of drugs), pathophysiology (the study of changes in the body due to disease), or clinical trials (studies used to test new treatments). If you're looking for a specific medical term, could you please provide more context?

I'm sorry for any confusion, but "Social Sciences" is a broad term that refers to academic disciplines that study human society and social relationships. It includes fields such as anthropology, sociology, psychology, political science, and economics. These subjects are considered part of the liberal arts and humanities, not medical sciences.

However, aspects of social sciences can intersect with medical studies in areas like medical anthropology, health psychology, sociology of health and illness, and psychiatry. For instance, medical anthropologists might study how cultural factors influence healthcare practices, while health psychologists examine the role of behavior and mental processes in health and illness.

If you're looking for a definition related to medical sciences, perhaps there was some confusion with the term. Could you please clarify or provide more context?

Research personnel, in the context of medical and scientific research, refers to individuals who are involved in the design, conduct, or reporting of research studies. This can include, but is not limited to, principal investigators, co-investigators, research assistants, research coordinators, data managers, biostatisticians, and laboratory technicians. These individuals may have various levels of education, training, and expertise, and their roles and responsibilities will depend on the specific research study and their individual qualifications. It is important for research personnel to adhere to ethical guidelines and regulations in order to ensure the integrity and validity of research findings.

ISBN 0-471-00710-2. Casella, George; Berger, Roger L. (2002). Statistical Inference (Second ed.). Thomson Learning. pp. 34-37. ... The terms probability distribution function and probability function have also sometimes been used to denote the probability ... Function related to statistics and probability theory List of probability distributions Probability amplitude - Complex number ... Therefore, the probability that the bacterium dies at 5 hours can be written as (2 hour−1) dt. This is the probability that the ...
ISBN 0-471-63729-7. Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer. pp. 21-24. ISBN 978-0-387 ... The posterior probability is a type of conditional probability that results from updating the prior probability with ... In variational Bayesian methods, the posterior probability is the probability of the parameters θ {\displaystyle \theta } given ... this probability equals 0.4. P ( B ) {\displaystyle P(B)} , or the probability that the student is not a girl (i.e. a boy) ...
... in the field of machine learning. The use of Bayesian probabilities as the basis of Bayesian inference has been supported by ... Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the ... Bayesian probability (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is an interpretation of the concept of probability, in which ... Broadly speaking, there are two interpretations of Bayesian probability. For objectivists, who interpret probability as an ...
Murphy, KP (2012). Machine learning: a probabilistic perspective. The MIT Press. (Articles needing additional references from ... Then, the conditional probability table of x 1 {\displaystyle x_{1}} provides the conditional probability values P ( x 1 = a k ... the column sum 6/9 is the marginal probability that x=0. If we want to find the probability that y=0 given that x=0, we compute ... The first column sum is the probability that x =0 and y equals any of the values it can have - that is, ...
Australia Belmont, CA: Wadsworth/Thomson Learning. ISBN 978-0534557379. Wikimedia Commons has media related to Probability ... mathematicians interpret the probability values of probability theory. There are two broad categories of probability ... as opposed to the term chance for a propensity probability. Some examples of epistemic probability are to assign a probability ... 3.1 Classical Probability 3.2 Logical Probability 3.3 Subjective Probability 3.4 Frequency Interpretations 3.5 Propensity ...
Li, Shengxi; Yu, Zeyang; Xiang, Min; Mandic, Danilo (2020). "Reciprocal Adversarial Learning via Characteristic Functions". ... Characteristic functions can be used as part of procedures for fitting probability distributions to samples of data. Cases ... The characteristic function exists for all probability distributions. This is not the case for the moment-generating function. ... The characteristic function is closely related to the Fourier transform: the characteristic function of a probability density ...
Fortuin, Vincent (2022). "Priors in Bayesian Deep Learning: A Review". International Statistical Review. 90 (3): 563-591. doi: ... A prior probability distribution of an uncertain quantity, often simply called the prior, is its assumed probability ... Some attempts have been made at finding a priori probabilities, i.e. probability distributions in some sense logically required ... and instead of the probability in phase space, one has the probability density Σ := P Tr ( P ) , N = Tr ( P ) = c o n s t ...
Cengage Learning, p. 437, ISBN 978-1-111-79878-9. Dodge, Y. (2003). The Oxford Dictionary of Statistical Terms. OUP, ISBN 0-19- ... The nominal coverage probability is often set at 0.95. By contrast, the (true) coverage probability is the actual probability ... the nominal coverage probability will equal the coverage probability (termed "true" or "actual" coverage probability for ... The "probability" in coverage probability is interpreted with respect to a set of hypothetical repetitions of the entire data ...
P. Walley (1996). Inferences from multinomial data: learning about a bag of marbles. Journal of the Royal Statistical Society, ... Traditional probability sufficient. Some critics of p-boxes argue that precisely specified probability distributions are ... P-boxes can arise from computations involving probability distributions, or involving both a probability distribution and an ... The sum of two random variables characterized by well-specified probability distributions is another precise probability ...
ISBN 978-1-139-49113-6. Pólya, George (1984). Probability; Combinatorics; Teaching and learning in mathematics. Rota, Gian- ... Thus, if the junction has seven exits the person will go to each one with probability one-seventh. This is a random walk on a ... In a simple symmetric random walk on a locally finite lattice, the probabilities of the location jumping to each one of its ... In psychology, random walks explain accurately the relation between the time needed to make a decision and the probability that ...
Solomonoff, R., "The Kolmogorov Lecture: The Universal Distribution and Machine Learning" The Computer Journal, Vol 46, No. 6 p ... A low-probability observation string is one that can only be generated by a long computer program. Algorithmic probability is ... The universal probability distribution is the probability distribution on all possible output strings with random input, ... A single universal prior probability that can be substituted for each actual prior probability in Bayes's rule was invented by ...
Through the rules of probability, the probability of a conclusion and of alternatives can be calculated. The best explanation ... Ray, Oliver (Dec 2005). Hybrid Abductive Inductive Learning (Ph.D.). University of London, Imperial College. CiteSeerX 10.1. ... "probability logic", following E. T. Jaynes). Bayesians identify probabilities with degrees of beliefs, with certainly true ... and certainly false propositions having probability 0. To say that "it's going to rain tomorrow" has a 0.9 probability is to ...
This law describes the relationship between prior and posterior probabilities when new facts are learnt. Written as quantities ... Inductive probability combines two different approaches to probability. Probability and information Probability and frequency ... The probability estimates given by it do not always obey the law of total of probability. Applying the law of total probability ... Prior probabilities are the probabilities before a fact is known. Posterior probabilities are after a fact is known. The ...
Probability-based samples implement a sampling plan with specified probabilities (perhaps adapted probabilities specified by an ... Government of Canada, Statistics Canada; Government of Canada, Statistics Canada (28 January 2009). "Learning resources: ... Inferences from probability-based surveys may still suffer from many types of bias. Surveys that are not based on probability ... Common methods of conducting a probability sample of the household population in the United States are Area Probability ...
Devore, Jay L. (2011). Probability and Statistics for Engineering and the Sciences (8th ed.). Boston, MA: Cengage Learning. pp ... Cengage Learning. pp. 185-226. ISBN 978-1-133-04979-1. Faherty, Vincent (2008). "Probability and statistical significance". ... Fisher suggested a probability of one in twenty (0.05) as a convenient cutoff level to reject the null hypothesis. In a 1933 ... This is the probability of not rejecting the null hypothesis given that it is true. Confidence levels and confidence intervals ...
Learned-Miller, E.; DeStefano, J. (2008). "A probabilistic upper bound on differential entropy". IEEE Transactions on ... The latter requirement simply means that all the nonzero probability mass of the distribution must be contained in some known ... In contrast, the order statistics-based bound introduced by Learned-Miller and DeStefano allows for an equal rate of violation ... A pointwise CDF bound is one which only guarantees their Coverage probability of 1 − α {\displaystyle 1-\alpha } percent on any ...
... and Learning Algorithms (PDF). Cambridge University Press. p. 540. ISBN 9780521642989. The probability distribution of a ... For a Gaussian process, continuity in probability is equivalent to mean-square continuity,: 145 and continuity with probability ... then the posterior probability, p ( θ ∣ D ) {\displaystyle p(\theta \mid D)} , i.e. the probability for the hyperparameters θ ... continuity in probability. Continuity in probability holds if and only if the mean and autocovariance are continuous functions ...
Physical Modelling Mathematics for CPS: Linear algebra (recalls); Probability and statistics (recalls); ODE; Fourier series & ... The Advanced Learning and Research Institute (ALaRI), a faculty of informatics, was established in 1999 at the University of ... Intelligent systems Supervised and unsupervised learning; Features extraction and selection; Recurrent networks (RC, ESN); ... Learning in a nonstationary environments; Cognitive fault diagnosis for CPS; Lab: adaptation and reliability in CPS. Cyber ...
Since quantification consists of generating a predicted probability distribution that estimates a true probability distribution ... the 2nd International Workshop on Learning to Quantify LeQua 2022: A machine learning competition on Learning to Quantify QuaPy ... In machine learning and data mining, quantification (variously called learning to quantify, or supervised prevalence estimation ... "LQ 2021: the 1st International Workshop on Learning to Quantify". "LQ 2022: the 2nd International Workshop on Learning to ...
In probability theory, p.d. kernels arise as covariance kernels of stochastic processes. Positive-definite kernels provide a ... This fact can be used to connect p.d. kernels with another interesting object that arises in machine learning applications, ... Loève, M. (1960). "Probability theory", 2nd ed., Van Nostrand, Princeton, N.J. Rosasco, L. and Poggio, T. (2015). "A ... There are several different ways in which kernels arise in probability theory. Nondeterministic recovery problems: Assume that ...
In turn, parameter compatibility is a probability measure that we derive from the probability distribution of the random ... Apolloni, B; Malchiodi, D.; Gaito, S. (2006). Algorithmic Inference in Machine Learning. International Series on Advanced ... Also Fraser's constructive probabilities devised for the same purpose do not treat this point completely. For x {\displaystyle ... Fraser, D. A. S. (1966). "Structural probability and generalization". Biometrika. 53 (1/2): 1-9. doi:10.2307/2334048. JSTOR ...
Estes, W.K. & Burke, C. J. A theory of stimulus variability in learning. Psychological Review, 1953, 60, 276-286. LaBerge, D. ( ... LaBerge, D. and Tweedy, J.R. (1964). Presentation probability and choice time. Journal of Experimental Psychology, 68, 477-481 ... In R.R. Bush & W.K. Estes (Eds.), Studies in Mathematical Learning Theory. Stanford: Stanford University Press, pp 53-93. ... LaBerge, D. (1973a) Attention and the measurement of perceptual learning. Memory and Cognition, 1, 268-276. LaBerge, D. and ...
It is defined by its closely related concept, frequentist probability. This entails a view that "probability" is nonsensical in ... Thiessen, Erik D. (5 January 2017). "What's statistical about learning? Insights from modelling statistical learning as a set ... For example, while humans do not usually excel at conditional probability calculations, the use of conditional probability ... probability as an intrinsic property of an event. Bayesian inference generally emphasizes the subjective probability of a ...
"No free lunch versus Occam's razor in supervised learning." In Algorithmic Probability and Friends. Bayesian Prediction and ... Therefore, if we have a "good" problem in practice or if we can choose a "good" learning algorithm for a given particular ... In essence, this says that when all functions f are equally likely, the probability of observing an arbitrary sequence of m ... To illustrate one of the counter-intuitive implications of NFL, suppose we fix two supervised learning algorithms, C and D. We ...
... there are two views on Bayesian probability that interpret the probability concept in different ways. In probability, a ... Many modern machine learning methods are based on objectivist Bayesian principles. According to the objectivist view, the rules ... According to his theory, a probability assertion is akin to a bet, and a bet is coherent only if it does not expose the wagerer ... Theory of Probability (2 vols.), J. Wiley & Sons, Inc., New York). ISBN 978-0471201427 (Articles with short description, Short ...
Scheaffer, Richard L.; Young, Linda J. (28 August 2009). Introduction to Probability and Its Applications. Cengage Learning. pp ... John Edmund Kerrich (1903-1985) was a mathematician noted for a series of experiments in probability which he conducted while ... was widely cited as evidence of the asymptotic nature of probability. It is still regarded as a classic study in empirical ... to show that it too tended towards a stable asymptotic state with probability of approximately 70 percent. In addition, the ...
Wiley Series in Probability and Statistics. Vol. 702. John Wiley & Sons. p. xv. ISBN 9780470237991. Bro, Rasmus (20 November ... Multilinear subspace learning Coppi, R.; Bolasco, S., eds. (1989). Multiway Data Analysis. Amsterdam: North-Holland. ISBN ...
PHI Learning Pvt. Ltd. p. 3. Das, Gurcharan (2002). India Unbound From Independence to the Global Information Age. Penguin ... "Norbert Wiener and Probability Theory" (PDF). Indian Academy of Sciences. Ghosh, Maiti & Bera 2010, pp. 1031-1032 Sarma. ... probability theory and stochastic processes, and the other two issues form the Series B, containing articles on applied ... Sinha felt the necessity of forming a specialized institute to facilitate research and learning of statistics. On 17 December ...
The probability that a particular diner samples a particular dish is proportional to the popularity of the dish among diners so ... Journal of Machine Learning Research. 12: 2461-2488. Zhou, Mingyuan; Carin, Lawrence (2012). "Negative Binomial Process Count ... Here is one way to derive this partition probability. Let C i {\displaystyle C_{i}} be the random block into which the number i ... In probability theory, the Chinese restaurant process is a discrete-time stochastic process, analogous to seating customers at ...
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ISBN 0-471-00710-2. Casella, George; Berger, Roger L. (2002). Statistical Inference (Second ed.). Thomson Learning. pp. 34-37. ... The terms probability distribution function and probability function have also sometimes been used to denote the probability ... Function related to statistics and probability theory List of probability distributions Probability amplitude - Complex number ... Therefore, the probability that the bacterium dies at 5 hours can be written as (2 hour−1) dt. This is the probability that the ...
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Under certain additional requirements, necessary and sufficient conditions are given to have, with probability one, an infinite ... Centro de Investigación en Matemáticas (CIMAT) , CIMAT · Department of Probability and Statistics ...
... views of anthropic probability, like SSA or SIA: instead it can, in some sense, lear… ... So we can define a robustly selfish agent by giving it a prior over different kinds of selfishness, and then letting it learn. ... So from the perspective of either view, it will look like UDT is learning that view, though from an agnostic perspective we ... Today I realized that UDT doesnt have to clash with "objective" views of anthropic probability, like SSA or SIA: instead it ...
... determine the probability of these influencing a particular outcome, and use that ... to try Dummies newest way to learn. By checking this box, you agree to the Terms of Use and Privacy Policy & to receive ... Use Statistics and Probability to Make Financial Forecasts. By: Michael Taillard and ... Using statistics and probability takes several different variables (the components of the different financial metrics), weights ...
Probability questions on the GED Math text will often ask you to calculate single or multiple probabilities. The following ... to try Dummies newest way to learn. By checking this box, you agree to the Terms of Use and Privacy Policy & to receive ... The probability of snow on Tuesday is 20%. Therefore, the probability that it will not snow on Tuesday is 100% - 20% = 80%. ... Probability questions on the GED Math text will often ask you to calculate single or multiple probabilities. The following ...
  • Thus, we report a statistical mismatch negativity (sMMN) that reflects statistical learning of transitional probability distributions that go beyond auditory sensory memory capabilities. (uib.no)
  • This course will focus on applying the calculus-based techniques learned in Mathematical Background for Biostatistics to the study of probability and statistical distributions. (edu.au)
  • This course begins with the study of probability, random variables, discrete and continuous distributions, and the use of calculus to obtain expressions for parameters of these distributions such as the mean and variance. (edu.au)
  • A probability density function is most commonly associated with absolutely continuous univariate distributions. (wikipedia.org)
  • This Probability tutorial will teach you the basics of probability theory, including what probability is, the different types of probability, how to calculate probability, probability distributions, and probability problems. (intellipaat.com)
  • Statistical Analysis uses probability distributions and theories to make any data calculations and present it via graphs, charts, and pictographs. (intellipaat.com)
  • You will also get familiar with grouped frequencies, graphical descriptions, probability distributions of discrete and continuous variables, The Normal Distribute (most important of all distributions) and Sampling and Combination of variables. (intellipaat.com)
  • There are many different types of probability distributions. (unofficed.com)
  • This project aims to uncover theoretical properties and new applications of perturbation models, a family of probability distributions for high dimensional structured prediction problems. (mit.edu)
  • To ensure safe development of the financial and insurance industry and promote the continuous growth of the social economy, the theory and its role of deep learning are firstly analyzed. (hindawi.com)
  • to present a range of research in RP theory for machine learning. (rss.org.uk)
  • Be aware, however, our intention is not to develop better and faster algorithms for deep learning, but to touch upon some mathematical theories which might (or might not, who knows) be important for a sound mathematical theory of deep learning. (uni-saarland.de)
  • I will use some simple examples and proofs from Machine Learning applied to regression and classification tasks, and draw parallels with some basic quantum theory ideas. (videolectures.net)
  • One of the most dreaded courses during my under-graduation is Probability, Statistics & Queuing Theory . (chandoo.org)
  • A good understanding of statistics & probability theory is necessary if you want to model complex real-life problems using Excel or similar tools. (chandoo.org)
  • Intuitive explanations are supported with an abundance of examples to give readers a thorough introduction to both the theory and applications of probability. (pearson.com)
  • In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would be equal to that sample. (wikipedia.org)
  • Probability theory deals with the analysis of random events. (elmens.com)
  • Today, if you are learning or teaching probability theory, each bit of misleading, confusing, or just wrong terminology or notation will cause you a bit of pain - a bit of extra effort and puzzlement that you need not have suffered. (workinginuncertainty.co.uk)
  • In his book, Foundations of The Theory of Probability , Andrey Kolmogorov provided a presentation of basic probability theory that has been translated and copied repeatedly every since. (workinginuncertainty.co.uk)
  • In elementary probability theory the word 'experiment' is used to refer to anything that produces data, not just to refer to experiments. (workinginuncertainty.co.uk)
  • By the end of this Probability tutorial, you will have a good understanding of the basics of probability theory and how to use it to solve problems. (intellipaat.com)
  • Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. (pdfchm.net)
  • It presents many concepts and results of probability theory and stochastic processes. (pdfchm.net)
  • We aim to understand theory and applications of diversity-inducing probabilities (and, more generally, 'negative dependence') in machine learning, and develop fast algorithms based on their mathematical properties. (mit.edu)
  • The Centre for Linguistic Theory and Studies in Probability (CLASP) is based in FLoV at the University of Gothenburg, and is funded by a 10 year grant from the Swedish Research Council (2015-2025). (lu.se)
  • CLASP is devoted to research and advanced training in the application of probabilistic modeling and machine learning methods to core issues in linguistic theory and cognition. (lu.se)
  • It combines many disciplines like statistics, mathematics, algorithms, and probability - one of which is empirical probability. (elmens.com)
  • For predictive modeling, you also need to understand the concept of probability, which forms the basis of many machine learning algorithms like logistic regression. (pluralsight.com)
  • Whether you belong to the field of Data Science , Big data Analysis , or Business Intelligence , learning statistics and probability can be of great help to improve business performance, handle and exhibit the data available and apply various logical algorithms, functions, and methods on that data. (intellipaat.com)
  • By grouping these predictors into 11 conceptual categories (such as demographic characteristics, COVID-19-linked stressors, or mental disorder comorbidities) and using machine learning algorithms, the investigators were able to predict in an individualized manner the probability of remission for participants in each of the groups. (medscape.com)
  • For VL, spatial data mining models were developed by integrating Machine Learning algorithms into a GIS-based modeling approach. (lu.se)
  • Level Sets and Extrema of Random Processes and Fields discusses how to understand the properties of the level sets of paths as well as how to compute the probability distribution of its extremal values, which are two general classes of problems that arise in the study of random processes and fields and in related applications. (pdfchm.net)
  • If the three coins are simultaneously tossed again, compute the probability of 2 heads coming up. (horizonlearning.in)
  • Conditional probability is also covered. (waikato.ac.nz)
  • Conditional probability is the possibility of an event/outcome occurring based on an existing event/outcome. (kdnuggets.com)
  • Conditional Probability helps Data Scientists produce more accurate models and outputs by using other variables in the dataset. (kdnuggets.com)
  • In this tutorial, we will cover a range of topics that are going to refurbish your mathematics, statistics and probability knowledge from school and college times. (intellipaat.com)
  • As we say, science, technology and mathematics are directly proportional to practice and practical implementation, each topic in this learning reference is thoroughly explained using real-time examples, which are easy-to-comprehend and memorize. (intellipaat.com)
  • Probability is a branch of mathematics that deals with the likelihood of events, while statistics is a field of study that deals with the collection, analysis, interpretation, and presentation of data. (intellipaat.com)
  • i) Find the probability that a student obtained less than 20 % in the mathematics test. (horizonlearning.in)
  • We see some of the same issues in Machine Learning and inference from probabilistic estimators in data-driven modelling. (videolectures.net)
  • We can never know everything about a situation, and this gives us our link between quantum mechanics and statistical inference through machine learning. (videolectures.net)
  • An introduction is presented in which the editor discusses various reports within the issue on topics including statistical inference, teaching of probability and statistics at school level and research on learning and teaching probability. (edu.au)
  • We examine the efficacy of various approximate inference methods for learning probabilistic models. (mit.edu)
  • There are many different things that are all called 'probability', such as Bayesian probability (non-negative by definition), frequentist probability (non-negative by definition), estimated probability, etc. (videolectures.net)
  • Artificial neural networks, fuzzy models and Bayesian probability models were all utilized to identify the most susceptible areas for a fatal disease incidence. (lu.se)
  • What is empirical probability? (elmens.com)
  • The empirical probability goes a bit further and determines how many times an event occurred divided by the number of trials. (elmens.com)
  • Empirical probability is used in Machine Learning, where computers learn patterns without you specifically teaching them. (elmens.com)
  • How Does Empirical Probability Work in Machine Learning? (elmens.com)
  • Empirical probability finds its way into complex Machine Learning technology in various ways. (elmens.com)
  • Understanding what is empirical probability and acing it is a significant step towards reaching your goal. (elmens.com)
  • If we want to find the probability of heads, it would be 1 (Head) / 2 (Heads and Tails) = 0.5. (kdnuggets.com)
  • For example, if you're working for an insurance company, you may want to find the probability of a person being able to pay for his insurance based on the condition that they have taken out a house loan. (kdnuggets.com)
  • Find the probability that a student of the class was born in August. (horizonlearning.in)
  • ii) Find the probability that a student obtained marks 60 or above. (horizonlearning.in)
  • In this article, the content for week 2 is outlined and the importance of probability discussed. (futurelearn.com)
  • Within the framework of statistical learning, many behavioural studies investigated the processing of unpredicted events. (uib.no)
  • However, surprisingly few neurophysiological studies are available on this topic, and no statistical learning experiment has investigated electroencephalographic (EEG) correlates of processing events with different transition probabilities. (uib.no)
  • We carried out an EEG study with a novel variant of the established statistical learning paradigm. (uib.no)
  • Our results reveal that, when predictions are based on statistical learning, events that do not match a prediction evoke an early anterior negativity, with the amplitude of this mismatch response being inversely related to the probability of such events. (uib.no)
  • A probability distribution is a statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. (unofficed.com)
  • A probability distribution is a statistical function that helps to describe the possible values and probabilities for a random variable within a given range. (kdnuggets.com)
  • How to calculate ctc probability for given input and expected output? (edureka.co)
  • In activity two, we'll talk about how to calculate the probability of combinations of random events occurring. (futurelearn.com)
  • The following practice questions ask you to do one ","noIndex":0,"noFollow":0},"content":"Probability questions on the GED Math text will often ask you to calculate single or multiple probabilities. (dummies.com)
  • description":"Probability questions on the GED Math text will often ask you to calculate single or multiple probabilities. (dummies.com)
  • Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. (pdfchm.net)
  • In activity four, we discuss how we can take our understanding of probability and random variables to make decisions even when outcomes are uncertain. (futurelearn.com)
  • Using a simple example, let's look at tossing a coin: either heads (H) or tails (T). Your probability will be the number of ways an event can occur divided by the total number of possible outcomes. (kdnuggets.com)
  • When using the Bernoulli distribution, we have the probability of one of the outcomes (p) and we can deduct it from the total probability (1), represented as (1-p). (kdnuggets.com)
  • From an observational dataset, our methods learn to automatically identify beneficial actions that will improve outcomes, rather than requiring human-made decisions. (mit.edu)
  • But I will argue that the use of Machine Learning to represent or simulate the universe only allows generically non-positive probabilities! (videolectures.net)
  • So the issue is not just connected with quantum mechanics, but is a more generic problem related to trying to simulate even classical probabilities by Machine Learning ideas. (videolectures.net)
  • How to simulate first passage time probability in python for a random walk? (edureka.co)
  • This video introduces terms and definitions, experimental probability and theoretical probability, and complementary events. (waikato.ac.nz)
  • How to save classifier to disk in scikit-learn? (edureka.co)
  • Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of. (lu.se)
  • We intend to showcase the innovations occurring at these intersections with this series of blogs and hope to motivate a Cambrian explosion in industrial applications of probabilistic deep learning techniques. (tensorflow.org)
  • Expected value considers the probability of each possible outcome and leads to long-term profitability. (learn-texas-holdem.com)
  • To forecast your finances, you watch for trends, patterns, and relationships, determine the probability of these influencing a particular outcome, and use that to model your forecast. (dummies.com)
  • Probability is the measure of a specific event or outcome occurring. (kdnuggets.com)
  • Learning Games for Kids is sponsored by Time4Learning, a convenient, online home education program for homeschooling , afterschool , and summer learning , and Time4MathFacts, with math facts practice games to learn the multiplication tables and the addition math facts , as well as subtraction and addition. (learninggamesforkids.com)
  • this project aims to scale up geometry-aware techniques for use in machine learning settings with lots of data, so that this structure may be utilized in practice. (mit.edu)
  • It also helps miners to learn and practice these cognitive skills. (cdc.gov)
  • Probability is an important mathematical function in our high education syllabus. (blogspot.com)
  • One quite well known ambiguity in common mathematical notation is a particular problem in probability work. (workinginuncertainty.co.uk)
  • We deal with this potential confusion by learning to recognize the names of the common mathematical functions, such as sin, cos, tan, log, ln, and exp. (workinginuncertainty.co.uk)
  • Most topics here contain explanations relating to mathematical interest to keep up your attention and concentration towards learning. (intellipaat.com)
  • Here are graphs of three possible functions that can be used for eliciting probabilities from students. (blogspot.com)
  • This is absolutely very good post about probabilities with details about the function with graphs. (blogspot.com)
  • Graphs are best option to know the result or probabilities as you can see the clear response. (blogspot.com)
  • This is really very good article about probability with nice graphs. (blogspot.com)
  • The neural networks of modern deep learning are in some sense a special class of functions of many variables, built out of (random) matrices and also some entry-wise non-linear functions. (uni-saarland.de)
  • At last, the data training indicates that the model designed by the deep learning method can accurately and effectively predict the basic situation of the financial and insurance industry, the minimum error can reach 0, and the highest is only about 3. (hindawi.com)
  • Statistics and now machine learning have achieved considerable success in working with multimodal data streams. (rss.org.uk)
  • In this simple tutorial, learn how to use Excel's FREQUENCY formula to generate frequency distribution of given data. (chandoo.org)
  • Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. (stackexchange.com)
  • For example, in the Empirical Bayes Methods, data collected is updated based on a prior probability. (elmens.com)
  • 3. Get updated prior assumption as a prior probability and run it on the full data set. (elmens.com)
  • The most prevalent form is supervised learning, where the data gets labeled, and the machine looks for exact patterns. (elmens.com)
  • Unsupervised learning has no data labels, making the machine look for various patterns. (elmens.com)
  • In this guide, you will learn the techniques of summarizing data and deducing probabilities in R. (pluralsight.com)
  • After learning through this Probability tutorial, you can also enroll in our Data Science courses . (intellipaat.com)
  • Why do you need to learn probability in data science? (kdnuggets.com)
  • Diagnosed attention deficit hyperactivity disorder and learning disability, United States, 2004-2006 : data from the National Health Interview Survey. (cdc.gov)
  • These AI and machine learning techniques hold much promise for the future of work to aid in complex data analysis. (cdc.gov)
  • I'm using the scikit-learn machine learning library (Python) for a machine learning project. (edureka.co)
  • What is the correct way of setting the attributes of the GaussianNB algorithm from scikit-learn library? (edureka.co)
  • You can't set class prior with the GaussianNB() function in scikit-learn. (edureka.co)
  • The three areas are: Logic Programming, Uncertainty Reasoning and Machine Learning. (pdfchm.net)
  • From Netflix recommendations to smart speakers, most platforms now use Machine Learning concepts. (elmens.com)
  • No, Probability and possibility are two different concepts. (intellipaat.com)
  • Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. (pdfchm.net)
  • Calculating poker odds involves determining the probability of winning a hand by considering the number of outs, or cards that can improve a player's hand. (learn-texas-holdem.com)
  • Considering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight corresponding brain regions. (rss.org.uk)
  • Guided cognitive behavioral therapy was associated with the highest probability of remission of anxiety and depression in 91.7% of students, the highest probability of remission of anxiety in all students, and the highest probability of remission of depression in 71.5% of participants. (medscape.com)
  • A series of studies examines whether certain biases in probability assessments and perceptions of loss, previously found in experimental studies, affect consumers' decisions about insurance. (ssrn.com)
  • At its heart I want to challenge the assumption that probabilities have to be positive. (videolectures.net)
  • blurb":"","authors":[{"authorId":8947,"name":"The Experts at Dummies","slug":"the-experts-at-dummies","description":"The Experts at Dummies are smart, friendly people who make learning easy by taking a not-so-serious approach to serious stuff. (dummies.com)
  • Based on consistent trends over each month of the last three years of a steady 1 percent monthly sales increase, you may predict that you'll continue to see steady growth over the next several years, but with a 68 percent probability of slowed growth as you find patterns where sales slowed every fourth year. (dummies.com)
  • Unfortunately, in probability work it is very common to use functions that have names that are single characters, such as P, p, E, and f x , and then go on to complicate matters even further by inventing new functions as a problem is solved, with names like φ, ψ L, and X. The potential for confusion is again increased. (workinginuncertainty.co.uk)
  • The videos of the High Dimensional Analysis: Random Matrices and Machine Learning course will be uploaded bit by bit to youtube. (uni-saarland.de)
  • In a more precise sense, the PDF is used to specify the probability of the random variable falling within a particular range of values, as opposed to taking on any one value. (wikipedia.org)
  • This is the longest description of an artefact that I have ever seen, where the 'artefact' is the appearance of negative probability in response to an approximation that was made earlier in the calculation. (videolectures.net)
  • Expected value (EV) takes into account both pot odds and probabilities, providing an overall assessment of how profitable an action may be in the long run. (learn-texas-holdem.com)
  • While the monetary policy stance is still accommodative, indicating a low recession probability, the negative inflation slope points to higher odds of a recession within a year. (ssrn.com)
  • With the expansion of science and technology, machine learning (ML) has become one of the main technologies for processing various tasks. (hindawi.com)
  • Deep learning (DL) technology, as a technology of machine learning, provides important technical support for the current blossom of various industries [ 1 ]. (hindawi.com)
  • In this talk I want to question parts of our working machinery we use in Machine Learning. (videolectures.net)
  • The core of the argument is that in modelling the universe through Machine Learning, we are obliged to make inferences based on finite and hence typically less-than-complete information. (videolectures.net)
  • The result is the new machine learning (ML)-powered Bundesliga Match Fact: Win Probability. (amazon.com)
  • Elevate Your Expertise with Our Machine Learning Certification Program! (edureka.co)
  • Machine Learning makes a note of all the noises that emerge from your mouth in the case of voice assistants. (elmens.com)
  • Machine Learning and AI are the two things that are the future of technology. (elmens.com)
  • We study a range of research areas related to machine learning and their applications for robotics, health care, language processing, information retrieval and more. (mit.edu)
  • Our goal is to develop methods that can 'explain' the behavior of complex machine learning models, without restricting their power. (mit.edu)
  • Many optimization problems in machine learning rely on noisy, estimated parameters. (mit.edu)
  • NIOSH anticipated that the coding process could be improved using a machine learning algorithm based on experience. (cdc.gov)
  • Through an arrangement between NIOSH and the Harvard Computer Society Tech for Social Good (T4SG) program, NIOSH asked T4SG to create an "auto-encoder" that would use machine learning based on previously coded datasets to assign industry codes to new datasets. (cdc.gov)
  • Based on initial tests, the machine learning algorithm was able to code up to 60% of the records with a high degree of reliability, surpassing the original target. (cdc.gov)
  • Through the collaboration with Harvard Computer Society T4SG, the Harvard computer science students applied their knowledge and skills in artificial intelligence (AI) and machine learning and helped NIOSH code a natural language database. (cdc.gov)
  • Please comment below on ways that you have used AI or machine learning in your work to advance occupational safety and health. (cdc.gov)
  • The main emphasis is on supervised machine learning methods for classification and prediction of tumor gene expression profiles. (lu.se)
  • These studies demonstrate the feasibility of machine learning-based molecular cancer classification. (lu.se)
  • It is a vital breakthrough to conduct a security evaluation of financial and insurance and ruin probability analysis through DL models. (hindawi.com)
  • The new Bundesliga Match Fact Win Probability was developed by building ML models that analyzed over 1,000 historical games. (amazon.com)
  • The first two sounds of all triplets were equiprobable, while the third sound occurred with either low (10%), intermediate (30%), or high (60%) probability. (uib.no)
  • Compared to high-probability triplet endings, endings with low and intermediate probability elicited an early anterior negativity that had an onset around 100 ms and was maximal at around 180 ms. This effect was larger for events with low than for events with intermediate probability. (uib.no)
  • By assigning a high prior probability to that class the recall should increase. (edureka.co)
  • High Probability ETF Trading by Larry Connors and Cesar Alvarez provides traders with 7 professional strategies to improve their ETF trading. (tradingmarkets.com)
  • When a stock moves beyond 1 SD, it essentially establishes a new trading range, and the probability of it following the same trend is notably high. (unofficed.com)
  • Those tests and tasks that demonstrated a high probability of predicting risk status were incorporated into a final battery. (cdc.gov)
  • Calculating equity in poker involves evaluating the probability of winning a hand based on the current cards and future community cards. (learn-texas-holdem.com)
  • Social media based sampling involves non-probability survey recruitment methods, such targeted ads appearing on the profile of targeted users, and online snowballing techniques, using digital platforms such as Facebook, Google, LinkedIn. (lu.se)
  • Perhaps you couldn't figure out what variables were influencing that slowed growth, but after calculating the probability of it, you were able to determine that your sales have a definite possibility of a temporary slow-down. (dummies.com)
  • Are probability and possibility the same? (intellipaat.com)
  • Probability is a measure of how likely an event is to occur, while possibility is a measure of whether an event can occur at all. (intellipaat.com)
  • The research results manifest that first, the designed security evaluation of the financial and insurance industry based on the deep learning and bankruptcy probability analysis model not only has strong learning ability but also can effectively reduce its own calculation error through short-time learning. (hindawi.com)
  • As one of the current main economic development channels, the financial and insurance industry provides significant support for the growth of the social economy, and its security evaluation and ruin probability analysis are the main development guarantees [ 2 ]. (hindawi.com)
  • In this, we will learn what is trend analysis & forecasting. (chandoo.org)
  • Adult learning theories, social learning theories, mentoring, and storytelling were all employed in the development of a series of safety training videos that have become very popular in the mining industry. (cdc.gov)
  • This definition may be extended to any probability distribution using the measure-theoretic definition of probability. (wikipedia.org)
  • Probability is the measure of the likelihood of an event/something happening. (kdnuggets.com)
  • The project studied children at risk for the kinds of performance deficits these tests endeavor to measure, but who themselves had no known exposure to neurotoxicants: Neonatal Intensive Care Unit (NICU) graduates, known to be at risk for both major and mild anomalies in perception, motor functioning, learning, memory and cognition. (cdc.gov)