Control hierarchical assumptions or processes models statistical. Medical search. Frequent questions

Nonparametric statistics can be used for descriptive statistics or statistical inference. (wikipedia.org)
That process ('active inference' in the sense of Friston et al. (springer.com)
Statistical inference: Basic principles, estimation and testing in the one- and two-sample cases (parametric and non-parametric). (up.ac.za)
Statistical inference in the simple regression case. (up.ac.za)
His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. (nips.cc)
Our group of scientists have PhDs in fields including psychology, marketing, and cognitive science, with domain knowledge in topics including decision making, motivation, and learning, methodological capabilities in experimental and quasi-experimental design, and statistical prowess in areas such as hierarchical modeling and causal inference approaches. (uber.com)
Constant-Time Probabilistic Learning & Inference in Hierarchical Sparse Distributed Representations , Invited Talk at the Neuro-Inspired Computational Elements ( NICE ) Workshop, Sandia Labs, Albuquerque, NM, Feb 2013. (brandeis.edu)

non-parametric hierarchical Bayesian models, such as models based on the Dirichlet process, which allow the number of latent variables to grow as necessary to fit the data, but where individual variables still follow parametric distributions and even the process controlling the rate of growth of latent variables follows a parametric distribution. (wikipedia.org)
The two effects further manifest in different aspects of RT distributions that can be captured by different components of the ex-Gaussian model using hierarchical Bayesian method. (springeropen.com)

These include, among others: Methods which are distribution-free, which do not rely on assumptions that the data are drawn from a given parametric family of probability distributions. (wikipedia.org)
However, especially for Boolean models (for details see below) this essentially assumes that a large number of species is either on or off with probability 1, a very unrealistic assumption. (biorxiv.org)
CBL) on a longitudinal corpus of child-caregiver interactions in English to test whether one proposed statistical learning mechanism-backward transitional probability-is able to predict children's speech productions with stable accuracy throughout the first few years of development. (philpapers.org)
We also discuss our approach as a radically new implementation of graphical probability modeling. (brandeis.edu)
In machine learning, a generative model is a model that captures the probability distribution of a dataset, typically so that new samples can be generated from it. (cityofmclemoresville.com)
For example, images can be modeled as two-dimensional arrays of pixel values, and thus a generative model for images might capture the joint probability distribution of all pixel values in all possible images. (cityofmclemoresville.com)

To appear in Stochastic Processes and Their Applications. (warwick.ac.uk)
In particular, it is increasingly recognized that commonly used ODE models are not able to capture the stochastic nature of many cellular processes. (biorxiv.org)
Often this inability comes from the fact that ODE models replace stochastic dynamics by some average. (biorxiv.org)
In contrast, such stochastic effects are taken into account by models based on the chemical master equation (CME). (biorxiv.org)
We review the current approaches to building process models on spheres, including the differential operator, the stochastic partial differential equation, the kernel convolution, and the deformation approaches. (projecteuclid.org)
The random utility functions of these models take the usual parametric form, while no distributional assumption is imposed on the stochastic disturbances. (repec.org)

Centers for Disease Control and Prevention. (cdc.gov)
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the US Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. (cdc.gov)

These techniques include, among others: non-parametric regression, which is modeling whereby the structure of the relationship between variables is treated non-parametrically, but where nevertheless there may be parametric assumptions about the distribution of model residuals. (wikipedia.org)
Multiple regression and correlation: Fitting and testing of models. (up.ac.za)
A classic model is logistic regression, where the log-odds of a subject having a phenotype is assumed to be linear in each of the variants. (biomedcentral.com)
The regression form contained within the lasso is a GLM, and so the model has all the versatility of that type of model, but the model selection is automated and the parameter coefficients for selected terms will not be the same. (variancejournal.org)
A highly informative statistical approach for semicontinuous outcomes is multilevel two-part modelling which treats the outcome as generated by a dual process, combining a multilevel logistic/probit regression for zeros and a multilevel (generalized) linear regression for nonzero values. (biomedcentral.com)
A multilevel two-part model combining a multilevel logistic regression to predict whether an individual eats and a multilevel gamma regression to predict how much is eaten, if an individual eats, is proposed. (biomedcentral.com)
The statistical analysis using multiple linear regression showed that hardiness is a personality trait that explains burnout, presenting different predictive models for each sample. (bvsalud.org)

The computational basis of perception, cognition, and action (if this ambitious story is on track) involves only three 'basic elements' - predictions (flowing from a long-term multi-level 'generative model'), prediction error signals (calculated relative to active predictions), and the estimated, context-varying 'precision' of those prediction error signals. (springer.com)
The rich, integrated (generative) model takes a highly distributed form, spread across multiple neural areas that may communicate in complex context-varying manners. (springer.com)
We claim that a basic disconnect between DL/ML and biology and the key to biological intelligence is that instead of FDR or localism, the brain uses sparse distributed representations (SDR), i.e., "cell assemblies", wherein items are represented by small sets of binary units, which may overlap, and where the pattern of overlaps embeds the similarity/statistical structure (generative model) of the domain. (brandeis.edu)
We've previously described an SDR-based, extremely efficient, one-shot learning algorithm in which the primary operation is permament storage of experienced events based on single trials (episodic memory), but in which the generative model (semantic memory, classification) emerges automatically, and as a computationally free, in terms of time and power, side effect of the episodic storage process. (brandeis.edu)
A particularly popular type of generative model is the generative adversarial network (GAN), which consists of two neural networks: a generator network that generates new samples, and a discriminator network that tries to correct the generator by flagging generated samples that it deems unreal. (cityofmclemoresville.com)

Package pvclust assesses the uncertainty in hierarchical cluster analysis. (howtolearnalanguage.info)

Typically, the model grows in size to accommodate the complexity of the data. (wikipedia.org)
These data provide important information that can be incorporated into risk models for ASFV transmission. (cdc.gov)
The procedures within IBM SPSS Statistics Base will enable you to get a quick look at your data, formulate hypotheses for additional testing, and then carry out a number of statistical and analytic procedures to help clarify relationships between variables, create clusters, identify trends and make predictions. (studentdiscounts.com)
Multiple statistical analysis: Bivariate data sets: Curve fitting (linear and non-linear), growth curves. (up.ac.za)
Several chapters also introduce statistical methods and procedures to allow readers to analyze behavioral data. (peterlang.com)
These models are typically obtained by a combination of expert and literature-driven knowledge as well as experimental data. (biorxiv.org)
To test their prediction, they used an original data set that consisted of organization-level data on HPWS and the downsizing process. (business-essay.com)
This CRAN Task View contains a list of packages that can be used for finding groups in data and modeling unobserved cross-sectional heterogeneity. (howtolearnalanguage.info)
Package genieclust implements a fast hierarchical clustering algorithm with a linkage criterion which is a variant of the single linkage method combining it with the Gini inequality measure to robustify the linkage method while retaining computational efficiency to allow for the use of larger data sets. (howtolearnalanguage.info)
Package idendr0 allows to interactively explore hierarchical clustering dendrograms and the clustered data. (howtolearnalanguage.info)
Package ClusterR implements k-means, mini-batch-kmeans, k-medoids, affinity propagation clustering and Gaussian mixture models with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. (howtolearnalanguage.info)
Individuals are usually part of a group having a thorough understanding of the methodology and measurement processes that have yielded the data to be evaluated, as well as the necessary expertise to identify potential sources of uncertainty in reported measurements for the assessment of the quality of measurement results. (degruyter.com)
These groups may also include experts such as statisticians and data scientists involved in the data evaluation process. (degruyter.com)
J. Polzehl , K. Tabelow , Magnetic Resonance Brain Imaging: Modeling and Data Analysis using R, 2nd Revised Edition , Series: Use R! (wias-berlin.de)
This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. (wias-berlin.de)
and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. (wias-berlin.de)
It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts. (wias-berlin.de)
The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. (wias-berlin.de)
The main chapters cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and Multi-Parameter Mapping. (wias-berlin.de)
For detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. (biomedcentral.com)
Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. (biomedcentral.com)
In this paper, we will focus on a dichotomous phenotype Y and aim to detect variants which affect Y based on case-control SNP data. (biomedcentral.com)
Given case-control SNP data, the simplest GWAS methods study variant sites individually [ 2 ]. (biomedcentral.com)
Dimensionality reduction Dimensionality reduction can be a helpful method for exploratory data analysis as well as modeling (feature engineering). (githubusercontent.com)
The relationship between data points is saved as a directed graph model where most points are not connected. (githubusercontent.com)
The company might collect data on customers over some time period, in order to model each customer's time to cancellation as a function of demographics or other predictors. (githubusercontent.com)
Computational models can help clarifying theories, and thus in delineating research questions, but also in facilitating experimental design, stimulus generation, and data analysis. (philpapers.org)
Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. (projecteuclid.org)
Over the past few decades, statisticians have developed covariance models that capture the spatial and temporal behavior of these global data sets. (projecteuclid.org)
We propose new identi cation and estimation approaches to semiparametric discrete choice models for bundles in both cross-sectional and panel data settings. (repec.org)
For the panel data model, we propose localized maximum score type estimators which have a non-standard asymptotic distribution. (repec.org)
2005). Among multivariate techniques, natural candidates for detecting the hierarchical structure of a set of data are hierarchical clustering methods (Anderberg, 1973). (isiarticles.com)
The brain is also massively parallel, but uses only 20 watts and moreover, the forms of MP used in DL, model / data parallelism and shared parameters, are patently non-biological, suggesting DL's core principles do not emulate biological intelligence. (brandeis.edu)
Compared to the number of territories identified based on spot mapping (197), distance sampling analysis of transect survey data provided a more accurate estimate of the abundance of male Bobolinks (230, 95% CI: 187, 282) than N‐mixture models of transect (668, 95% CI: 332, 1342) and point‐count (337, 95% CI: 203, 559) data. (researchgate.net)
Neural networks are a type of machine learning algorithm that are used to model complex patterns in data. (cityofmclemoresville.com)
Neural networks are similar to other machine learning algorithms, but they are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data. (cityofmclemoresville.com)
This creates a "memory" which allows the RNN to model temporal/sequential data. (cityofmclemoresville.com)
The specific form of the model depends on the type of data being generated. (cityofmclemoresville.com)
The lasso performs well in modeling, identifying known features in the synthetic data, and tracking them accurately. (variancejournal.org)
Many claim data sets are modeled, and estimates of loss reserve produced, by means of simple statistical structures. (variancejournal.org)
Other data sets are modeled by means of more complex statistical structures. (variancejournal.org)
For example, Taylor and McGuire (2016) describe in detail the application of generalized linear models ( GLMs ) to claims data. (variancejournal.org)
This approach is especially suitable for data sets that contain features such that the chain ladder model is inapplicable. (variancejournal.org)
This category of model includes the artificial neural net ( ANN ), which has been studied in earlier literature (Mulquiney 2006) , and shown to be well adapted to data sets with complex features, such as those modeled with GLMs. (variancejournal.org)
It will provide only limited revelation of the claim processes and mechanics (e.g., superimposed inflation ( SI )) generating the data set. (variancejournal.org)
Although the ANN might produce a good fit to past data, the absence of this information on claim processes might render their extrapolation to the future difficult. (variancejournal.org)
However, linear multilevel modelling which is commonly used for EMA data to account for repeated measures within individuals cannot be applied to semicontinuous outcomes. (biomedcentral.com)
However, the model suffers from limitations due to its design and its reliance on administrative data. (who.int)
The addition of key condition-specific clinical data points at the time of hospital admission will dramatical y improve model performance. (who.int)
2] A prediction model that and transparency of such risk adjustment models, and to widen uses a `history of coronary heart disease' as a risk factor to predict discussion on the strengths and limitations of risk adjustment models death from an acute myocardial infarction (AMI) is always going based on service claims data. (who.int)
Data sources: The data sources searched were MEDLINE, MEDLINE Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Database of Controlled Trials, Database of Abstracts of Reviews of Effects and the Health Technology Assessment. (bvsalud.org)

The modeling of the correlation matrix of a complex system with tools of hierarchical clustering has been useful in the multivariate characterization of stock return time series (Mantegna, 1999, Bonanno et al. (isiarticles.com)

Our results suggest that additional studies to evaluate model‐based estimates of abundance with the best available information (e.g., from spot mapping of marked or unmarked populations and nest monitoring) would be useful to ensure that robust estimates are provided to support population estimates and conservation actions. (researchgate.net)
Diagnostic test accuracy estimates were calculated using bivariate models and a summary receiver operating characteristic curve was calculated using a hierarchical model. (bvsalud.org)
Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. (cdc.gov)

I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. (umd.edu)
I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses a mathematical analysis of the effects of cultural transmission as the basis for an experimental method that magnifies the effects of inductive biases. (umd.edu)

Package dynamicTreeCut contains methods for detection of clusters in hierarchical clustering dendrograms. (howtolearnalanguage.info)
Many complex systems observed in the physical, biological and social sciences are organized in a nested hierarchical structure, i.e. the elements of the system can be partitioned in clusters which in turn can be partitioned in subclusters and so on up to a certain level (Simon, 1962). (isiarticles.com)
MOTIVATION: Hierarchical clustering of microbial genotypes has the limitation that hierarchical clusters are nested, where smaller groups of related isolates exist within larger groups that get progressively larger as relationships become increasingly distant. (cdc.gov)

In this paper, we review some nonparametric generalizations of Johnson's postulate for a class of nonparametric priors known as species sampling models. (projecteuclid.org)
In particular, we revisit and discuss the "sufficientness" postulate for the two parameter Poisson-Dirichlet prior within the more general framework of Gibbs-type priors and their hierarchical generalizations. (projecteuclid.org)

Inspired by the work of evo-devo biologists, evo-devo complexity theorists look for processes of evolutionary creativity and developmental constraint in any autopoetic complex systems, at any scale. (evodevouniverse.com)
Three applications are presented as examples of the type of complexity we face in computational modelling of complex systems of systems. (iospress.com)

The canonical assumptions of mainstream economics are these: Individuals seek to maximize satisfaction of preferences that are taken as given (exogenous). (democracyjournal.org)
The theory, Sparsey, was introduced 20 years ago as a canonical cortical circuit/algorithm model, but not elaborated as an alternative to PPC theories. (brandeis.edu)

As non-parametric methods make fewer assumptions, their applicability is much more general than the corresponding parametric methods. (wikipedia.org)
Also, due to the reliance on fewer assumptions, non-parametric methods are more robust. (wikipedia.org)
Statistical methods that can be used to deal with these problems are using cluster integration and path analysis [ 3 ]. (frontiersin.org)
We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. (biomedcentral.com)
Due to the retrospective nature of case-control studies, they can prove only association rather than clear causal relationships [ 10 ] (however see [ 28 ] for follow-up methods for finding such causal relationships). (biomedcentral.com)
Survival analysis (ISL Chapter 11) - Survival analysis is a class of statistical methods that analyze time-to-events. (githubusercontent.com)
Methods: We document the actual experience of type 2 OPV (OPV2) cessation and reconsider prior modeling assumptions related to OPV restart. (cdc.gov)

Because the biocompatibility properties resulting from this new MIM cobalt alloy process are not well understood, we conducted tests to evaluate cytotoxicity (in vitro), hemolysis (in vitro), toxicity effects (in vivo), tissue irritation level (in vivo), and pyrogenicity count (in vitro) on such samples. (iospress.com)
Find, evaluate, process, manage and present information resources for academic purposes using appropriate technology. (up.ac.za)
We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. (philpapers.org)

Nonparametric statistics is the type of statistics that is not restricted by assumptions concerning the nature of the population from which a sample is drawn. (wikipedia.org)
Nonparametric tests are often used when the assumptions of parametric tests are evidently violated. (wikipedia.org)

An overview of GWAS experimental methodology, confounding variables that must be controlled, statistical techniques for pre- and post-processing, limitations and applications can be found in [ 28 ], while a discussion of the clinical implications of GWAS results can be found in [ 15 ]. (biomedcentral.com)

The authors show, with a series of examples, how computational modeling can be integrated with empirical AGL approaches, and how model selection techniques can indicate the most likely model to explain experimental outcomes. (philpapers.org)
This centralized team set out to apply its methodological capabilities in experimental and quasi-experimental design and statistical expertise in areas such as hierarchical modeling to enhancing our products for the benefit of riders and driver-partners in diverse regions. (uber.com)
The study is quasi-experi- mental, which means that it uses a pre-post experimental method with two groups of students, one experimental group ( N = 15) and one control group ( N = 16). (lu.se)

Conversely, even a large prediction error signal, if it is assigned extremely low precision, may be rendered systemically impotent, unable to drive learning or further processing. (springer.com)
Prediction-driven learning, when implemented using hierarchical (hence multi-level) machinery automatically uncovers structure at multiple scales of space and time. (springer.com)
The Spatio-Temporal Prediction (STP) technique can fit linear models for measurements taken over time at locations in 2D and 3D space. (studentdiscounts.com)
Each higher level allows greater synoptic prediction (and thereby control), at the cost of local fidelity. (pfeilstor.ch)
When designing a risk prediction model, patient-proximate variables with a sound theoretical or proven association with the outcome of interest should be used. (who.int)
In particular, the limitations of machine learning prediction models should be understood, and these models should be appropriately developed, evaluated and reported. (who.int)

illustrate how empirical AGL studies can benefit from computational models and techniques. (philpapers.org)
While few economists endorse such an extreme interpretation of the gender wage gap, empirical research on the topic often takes the form of statistical models that ignore any measures of bargaining power other than unionization, and control for as many variables as possible-race, ethnicity, education, labor force experience, job tenure, hours of work, occupation, industry-in order to isolate the unexplained gender effect. (democracyjournal.org)

The variables of attitude, subjective norms, behavioral control, and intention to comply with paying explained the behavior of Bank X customers to comply with paying mortgages by 88.32%, while the remaining 11.68% was explained by variables outside the model. (frontiersin.org)

The present study aims to assess how simple physical effort operationalized as isometric muscle contractions affects visual attention and inhibitory control. (springeropen.com)
We found that visual search under physical effort is faster, but more vulnerable to distractor interference, potentially due to arousal and reduced inhibitory control, respectively. (springeropen.com)
For instance, a study found that inhibitory control predicted how much individuals snacked, whereas it did not predict whether individuals snacked [ 1 ]. (biomedcentral.com)

For the cross-sectional model, we propose a kernel-weighted rank procedure and establish N-asymptotic normality of the resulting estimators. (repec.org)

The hierarchical structure of interactions among elements strongly affects the dynamics of complex systems. (isiarticles.com)

Package hclust1d provides univariate agglomerative hierarchical clustering for a comprehensive choice of linkage functions based on an O ( n log ( n )) algorithm implemented in C++. (howtolearnalanguage.info)
With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS. (biomedcentral.com)

In an epidemiologic context, investigators must dissect hierarchical trees into discrete groupings that are epidemiologically meaningful. (cdc.gov)

Unified Theory for the Large Family of Time Varying Models with Arma Representations: One Solution Fits All. (repec.org)

IBM® SPSS® Statistics Base is easy to use and forms the foundation for many types of statistical analyses. (studentdiscounts.com)
In general, model reporting should conform to published reporting standards, and attempts should be made to test model validity by using sensitivity analyses. (who.int)
Map Work Group to help guide application of geographic part of spatial statistical analyses (7). (cdc.gov)

Predictive processing Footnote 1 (henceforth, PP) depicts perception, cognition, and action as the closely woven products of a single kind of inferential process. (springer.com)
Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition. (philpapers.org)
This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. (philpapers.org)
We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. (bmj.com)
Finally, future evo-devo models may require what [http://vcresearch.berkeley.edu/faculty/lucia-jacobs Lucia Jacobs] refers to as "cog-evo-devo" (Jacobs 2012), the recognition that both information and cognition evolve and develop, are causal agents in the dynamics of complex replicators, and are increasingly important in determining their future, via such mechanisms as collective ethics, empathy, and niche construction, as higher intelligence emerges. (evodevouniverse.com)

The goal is to use the obtained models to control power consumption and to build predictive models for production planning. (lu.se)

Notwithstanding these distinctions, the statistical literature now commonly applies the label "non-parametric" to test procedures that we have just termed "distribution-free", thereby losing a useful classification. (wikipedia.org)
Mechanistic models are commonly used to acquire insights about the biochemical reaction networks that govern cellular processes inside a cell. (biorxiv.org)
Models based on ordinary differential equations (ODE) are most commonly used [ 12 ]. (biorxiv.org)

Package mdendro provides an alternative implementation of agglomerative hierarchical clustering. (howtolearnalanguage.info)
Its general implementation in R, a widely used and freely available statistical software, using the R-package brms is described. (biomedcentral.com)

In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. (biomedcentral.com)

ConvNets are similar to other types of neural networks but they have an additional layer, called a convolutional layer, that enables them to better process spatial information. (cityofmclemoresville.com)

We recently described a statistical framework (Method A) for dissecting hierarchical trees that attempts to minimize investigator bias. (cdc.gov)
Here, we apply a modified version of that framework (Method B) to a hierarchical tree constructed from 2111 genotypes of the foodborne parasite Cyclospora, including 639 genotypes linked to epidemiologically defined outbreaks. (cdc.gov)
Source code (Method B) and instructions for its use are available here: https://github.com/Joel-Barratt/Hierarchical-tree-dissection-framework. (cdc.gov)

A typical approach uses the case-control study, which samples subjects with both phenotypes and looks for differences in the target variables between the two groups. (biomedcentral.com)
With our domain expertise, we provide insight into topics like how to increase customer satisfaction, and with our methodological and statistical expertise, we provide answers to questions like how to quantify the business impact of customer satisfaction (with mediation modeling being one such approach). (uber.com)
Pauline's research focuses on building such models through a physics-informed learning based approach, taking advantage of the available measurements. (lu.se)

They're bridged by the abduction stage, which occurs at multiple levels of abstraction and hierarchical (conscious and unconscious) processing, which transforms blurs of color and light into recognized objects,[^1] and then again into larger theories about the causal forces behind them, or the nature of existence. (pfeilstor.ch)

By building self-serve sample size and statistical analysis calculators with Shiny and an R package, we empowered non-technical teams to leverage our expertise. (uber.com)
Analysis and projection of financial statements, cost elements in pricing, cost control and design of accounting systems. (edu.pk)
The possibility of influencing change is addressed in terms of concepts, principles and models for analysis and design in a range of domains or contexts. (iospress.com)
His research focuses on text mining (opinion mining, text classification/summarization), Information Retrieval, Dialogue System (speech-act analysis, dialogue modeling). (umd.edu)

This is opposed to parametric statistics, for which a problem is restricted a priori by assumptions concerning the specific distribution of the population (such as the normal distribution) and parameters (such the mean or variance). (wikipedia.org)
There are two ways of speeding up MCMC algorithms: (1) construct more complex samplers that use gradient and higher order information about the target and (2) design a control variate to reduce the asymptotic variance. (warwick.ac.uk)
Specifically, for a d-dimensional Random walk Metropolis chain with an IID target I will present a control variate, for which the asymptotic variance of the corresponding estimator is bounded by a multiple of (log d)/d over the spectral gap of the chain. (warwick.ac.uk)

These processes are the dynamical and informational opposite of the predictable, information-conservative, convergent, unifying, and hierarchical processes of "development," which work to replicate and maintain that system. (evodevouniverse.com)

Use the Temporal Causal Modeling (TCM) technique to uncover hidden causal relationships among large numbers of time series and automatically determine the best predictors. (studentdiscounts.com)

Computational Psychiatry aims first to model the computations that the brain performs-that is, the brain's solutions to the problems it faces-and second to thereby understand how the 'abnormal' perceptions, thoughts and behaviours that are currently used to define psychiatric disorders relate to normal function and neural processes. (bmj.com)
Here, we discuss how to define and obtain hierarchical trees, correlation based trees and networks from a correlation matrix. (isiarticles.com)

The second meaning of non-parametric involves techniques that do not assume that the structure of a model is fixed. (wikipedia.org)
Finding Hierarchical Structure in Binary Sequences: Evidence from Lindenmayer Grammar Learning. (philpapers.org)
In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. (philpapers.org)
Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. (philpapers.org)
For each measure, a linear mixed effects model with dog ownership as a fixed effect, and a random effects structure of measurement point nested in participant nested in pair was used to assess the effect of dog ownership. (biomedcentral.com)
All-Payer Model for hospitals, which shifted the state's hospital payment structure from an all-payer hospital rate setting system to an all-payer global hospital budget that encompasses inpatient and outpatient hospital services. (who.int)

Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. (springer.com)

But the rise of theoretical and systems ecology and its models, including ecological energetics, panarchy, and ascendancy, can be viewed as supporting the idea that ecologies themselves both evolve and develop. (evodevouniverse.com)

We also discuss a method to associate a hierarchically nested factor model to a hierarchical tree obtained from a correlation matrix. (isiarticles.com)
He is a full professor at the Technical University of Munich, holding the chair 'Mathematical Modelling of Biological Systems', associate faculty at the Wellcome Trust Sanger Institute as well as adjunct faculty at the Northwestern University. (lu.se)

In these techniques, individual variables are typically assumed to belong to parametric distributions, and assumptions about the types of associations among variables are also made. (wikipedia.org)

The human brain, PP here suggests, commands a rich, integrated model of the worldly sources of sensory inputs, and uses that long-term model to generate on-the-spot predictions about the probable shape and character of current inputs. (springer.com)
The word 'technologies' as used here refers to both technologies and to meta-technologies that is, to the technologies limited in the range of inputs, outputs, and processing steps that characterized the industrial era as well as to the meta-technologies of unlimited inputs, outputs, and processing steps that characterize the information society (Braman, in press). (firstmonday.org)
but rather 7) noise is a resource generated/used to cause similar inputs to map to similar codes, controlling a tradeoff between storage capacity and embedding the input space statistics in the pattern of intersections over stored codes, indirectly yielding correlation patterns. (brandeis.edu)

2003), where the estimation of statistically reliable properties of the correlation matrix is crucial for several financial decision processes such as asset allocation, portfolio optimization (Tola et al. (isiarticles.com)
It is seen that the procedure can be readily adapted to the estimation of parameter and process error, but can also estimate one component of model error. (variancejournal.org)

Therefore a quantitative description of hierarchies of the system is a key step in the modeling of complex systems (Anderson, 1972). (isiarticles.com)
Is it an obstacle in our ability to build theories to control change in complex systems? (iospress.com)
These three applications - covering story generation in linguistics, network centric operations in defence and interdependency security problems - demonstrate how causal dependencies can be modelled, identified and extracted from a computational environment that mimics real-world complex systems of systems. (iospress.com)
Deep reinforcement learning algorithms have been used to solve complex tasks such as playing Go and Atari games from raw pixels, and controlling robotic arms to manipulate objects. (cityofmclemoresville.com)

A pragmatic trial using the "cohort multiple randomised controlled trial" design was used to test the effectiveness of adjunctive treatment by homeopaths compared to usual care alone, over a period of 12 months in patients with self-reported depression. (biomedcentral.com)
The previous IARC Monograph (71, 1999) reviewed seven cohort studies (photography film industry, textile fiber manufacturing industry and aircraft maintenance workers) and three case-control studies. (who.int)
2009), one new cohort study (Goldberg and Thériault, 1994a, 1994b), an update of one of the case-control studies (Dumas et al. (who.int)

Results showed that participants' pattern of anticipation could not be accounted for by "flat" statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. (philpapers.org)

Convolutional neural networks (also known as ConvNets or CNNs) are a type of neural network that are particularly well-suited for image processing tasks. (cityofmclemoresville.com)

The control variate is constructed using the solution of the Poisson equation for the scaling limit in the seminal paper 'Weak convergence and optimal scaling of random walk Metropolis algorithms' of Gelman, Gilks and Roberts. (warwick.ac.uk)

It is thus pivotal to understand how cognitive processes operate with concurrent actions. (springeropen.com)
Visual search, although a ubiquitous cognitive process in everyday life, is often studied in laboratory settings with precise control and manipulations of statically presented stimuli. (springeropen.com)
In addition, physical action often occurs concurrently and parallelly with cognitive processes. (springeropen.com)
Rosenbaum, 2017 ), it is thus pivotal to understand how concurrent physical action affects the ongoing cognitive processes, such as attention. (springeropen.com)
Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning. (philpapers.org)

We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. (bmj.com)

In this work, the visco-hyperelastic constitutive model of the tendon implemented through the use of three-parameter Mooney-Rivlin form and sixty-four-parameter Prony series were firstly analyzed using ANSYS FE software. (iospress.com)
Package protoclust implements a form of hierarchical clustering that associates a prototypical element with each interior node of the dendrogram. (howtolearnalanguage.info)

Deep learning models are composed of multiple layers of interconnected processing nodes (neurons). (cityofmclemoresville.com)

Vibration-Controlled Transient Elastography (VCTE) with Controlled Attenuation Parameter (CAP) is an effective, non-invasive and safe diagnostic method to estimate the degree of fibrosis and steatosis in the liver, but little is known about its applicability in the paediatric population. (bvsalud.org)

This is a reason why the less computationally expensive ODE models are popular. (biorxiv.org)

Anatomy1

Organisms1

Chemicals and Drugs4

Analytical, Diagnostic and Therapeutic Techniques and Equipment55

Psychiatry and Psychology6

Phenomena and Processes34

Disciplines and Occupations11

Anthropology, Education, Sociology and Social Phenomena4

Humanities2

Information Science16

Health Care49

Geographicals2

Inference7

Bayesian2

Probability6

Stochastic6

Centers for Diseas2

Regression7

Generative model5

Cluster analysis1

Data51

Multivariate1

Estimates3

Probabilistic2

Clusters3

Priors2

Complexity2

Canonical2

Methods7

Evaluate3

Nonparametric2

Methodology1

Experimental3

Prediction6

Empirical2

Behavioral1

Inhibitory control3

Cross-sectional1

Interactions1

Algorithm2

Discrete1

Representations1

Analyses3

Cognition5

Predictive models1

Commonly3

Implementation2

Markov1

Spatial1

Framework3

Approach3

Transforms1

Analysis4

Variance3

Dynamical1

Causal relationships1

Define2

Structure6

Graphical1

Theoretical1

Associate2

Typically1

Inputs3

Estimation2

Complex4

Cohort3

Participants1

Neural network1

Paper1

Cognitive5

Context1

Form2

Multiple1

Elastography1

Computationally1