Markov Chains: A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.Monte Carlo Method: In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)Bayes Theorem: 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.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.Models, Genetic: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.Models, Statistical: 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.Computer Simulation: Computer-based representation of physical systems and phenomena such as chemical processes.Likelihood Functions: Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.Stochastic Processes: Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.Phylogeny: The relationships of groups of organisms as reflected by their genetic makeup.Software: Sequential operating programs and data which instruct the functioning of a digital computer.Genealogy and HeraldryProbability: The study of chance processes or the relative frequency characterizing a chance process.Models, Biological: Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.Evolution, Molecular: The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.Computational Biology: A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.Chromosome Mapping: Any method used for determining the location of and relative distances between genes on a chromosome.Sequence Analysis, DNA: A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.Sequence Alignment: The arrangement of two or more amino acid or base sequences from an organism or organisms in such a way as to align areas of the sequences sharing common properties. The degree of relatedness or homology between the sequences is predicted computationally or statistically based on weights assigned to the elements aligned between the sequences. This in turn can serve as a potential indicator of the genetic relatedness between the organisms.Data Interpretation, Statistical: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.Models, Theoretical: Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.Pattern Recognition, Automated: In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)Biometry: The use of statistical and mathematical methods to analyze biological observations and phenomena.Biostatistics: The application of STATISTICS to biological systems and organisms involving the retrieval or collection, analysis, reduction, and interpretation of qualitative and quantitative data.Genetics, Population: The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.Polymerase Chain Reaction: In vitro method for producing large amounts of specific DNA or RNA fragments of defined length and sequence from small amounts of short oligonucleotide flanking sequences (primers). The essential steps include thermal denaturation of the double-stranded target molecules, annealing of the primers to their complementary sequences, and extension of the annealed primers by enzymatic synthesis with DNA polymerase. The reaction is efficient, specific, and extremely sensitive. Uses for the reaction include disease diagnosis, detection of difficult-to-isolate pathogens, mutation analysis, genetic testing, DNA sequencing, and analyzing evolutionary relationships.Quantitative Trait, Heritable: A characteristic showing quantitative inheritance such as SKIN PIGMENTATION in humans. (From A Dictionary of Genetics, 4th ed)Quantitative Trait Loci: Genetic loci associated with a QUANTITATIVE TRAIT.Molecular Sequence Data: Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.Genetic Markers: A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.Quality-Adjusted Life Years: A measurement index derived from a modification of standard life-table procedures and designed to take account of the quality as well as the duration of survival. This index can be used in assessing the outcome of health care procedures or services. (BIOETHICS Thesaurus, 1994)Cost-Benefit Analysis: A method of comparing the cost of a program with its expected benefits in dollars (or other currency). The benefit-to-cost ratio is a measure of total return expected per unit of money spent. This analysis generally excludes consideration of factors that are not measured ultimately in economic terms. Cost effectiveness compares alternative ways to achieve a specific set of results.Population Dynamics: The pattern of any process, or the interrelationship of phenomena, which affects growth or change within a population.Sequence Analysis, Protein: A process that includes the determination of AMINO ACID SEQUENCE of a protein (or peptide, oligopeptide or peptide fragment) and the information analysis of the sequence.Base Sequence: The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.Genetic Linkage: The co-inheritance of two or more non-allelic GENES due to their being located more or less closely on the same CHROMOSOME.Classification: The systematic arrangement of entities in any field into categories classes based on common characteristics such as properties, morphology, subject matter, etc.Population Density: Number of individuals in a population relative to space.Reproducibility of Results: The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.Multifactorial Inheritance: A phenotypic outcome (physical characteristic or disease predisposition) that is determined by more than one gene. Polygenic refers to those determined by many genes, while oligogenic refers to those determined by a few genes.Probability Learning: 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.Artificial Intelligence: 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.Cluster Analysis: A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.Normal Distribution: Continuous frequency distribution of infinite range. Its properties are as follows: 1, continuous, symmetrical distribution with both tails extending to infinity; 2, arithmetic mean, mode, and median identical; and 3, shape completely determined by the mean and standard deviation.Pedigree: The record of descent or ancestry, particularly of a particular condition or trait, indicating individual family members, their relationships, and their status with respect to the trait or condition.Genetic Variation: Genotypic differences observed among individuals in a population.Genotype: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.Alleles: Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.

*  Free The Markov Chain Algorithm Download
The Markov Chain Algorithm 1.2 is A classic algorithm which can produce entertaining output, given a sufficiently ... chain-of-memories , chain transmission , markov , audio soundmax 4 xl , mpeg4 maker , value chain , chain store , integrator. ... daisy chain , offline chain , 3230 mobile software , evoiz dialer 311 , chain link gates , usb memory key , gom lab eng , chain ... For The Markov Chain Algorithm 1.2 Tags. super ramdisk plus , xbox dvd covers , food chain games , diktator simulator , food ...
*  Interacting Markov Chain Monte Carlo Methods For Solving Nonlinear Measure-Valued Equations - Semantic Scholar
Markov chain Monte Carlo methods, sequential Monte Carlo, self-interacting processes, time-inhomogeneous Markov chains, ... The associated stochastic processes belong to the class of self-interacting Markov chains. In contrast to traditional Markov ... v er si on 4 5 Fe b 20 08 Interacting Markov Chain Monte Carlo Methods 3 ... Ces algorithmes stochastiques appartiennent à la classe des modèles non linéaires de châınes de Markov en autointeraction. A la ...
*  Predicting CpG Islands and Their Relationship with Genomic Feature in Cattle by Hidden Markov Model Algorithm
We used hidden markov model algorithm to detect CGIs. The total number of predicted CGIs for cattle was 90668. The number of ... Assume that Y(s) is a stationary first-order Markov chain. The choice of the state is based on two HMM. One is for GC content ... We used hidden markov model algorithm to detect CGIs. The total number of predicted CGIs for cattle was 90668. The number of ... Wu H., Caffo B., Jaffee H.A., Irizarry R.A. and Feinberg A.P. (2010). Redefining CpG islands using hidden Markov models. ...
*  The Genealogical World of Phylogenetic Networks: August 2013
In phylogenetics based on Markov Chain Monte Carlo (MCMC) methods, which produce a set of trees sampled in proportion to their ...
*  Linear Models and Markov Chain MBA Assignment Help, Online Business Assignment Writing Service and Homework Help
Online MBA Assignment Writing Service and Homework Help Linear Models and Markov Chain Assignment Help Linear models explain a ... Linear Models and Markov Chain MBA Assignment Help, ... Linear Models and Markov Chain. Linear Models and Markov Chain ... A Markov chain is a stochastic procedure with the Markov home. The term "Markov chain" describes the series of random variables ... A Markov Chain is a random procedure that goes through shifts from one state to another on a state area. A Markov chain is a ...
*  Inference With Non-Gaussian Ornstein-Uhlenbeck Processes for Stochastic Volatility
The algorithm is based on Markov chain Monte Carlo methods and we use a series representation of Levy processes. Inference for ... Keywords: Bayesian methods; Deposit spot rate; Levy process; Markov chain Monte Carlo; Stock price; Other versions of this item ... The algorithm is based on Markov chain Monte Carlo methods and we use a series representation of Levy processes. Inference for ...
*  Markov Chains - Artificial Intelligence -
A brief introduction to Markov Chains. (also called Markov Models, Hidden Markov Models).. Markov Chains are models for the ... The Markov chain arises because we run this system over many such time steps. The name also arises from the fact that Markov ... "Markov chains". Some of the first of them were:. http://forum. ... I found out about Hidden Markov Models, but they seem very Mathsy for me.... Many of the uses of Hidden Markov Models (HMMs) to ...
*  Simulate Random Walks Through Markov Chain
Create Markov Chain From Random Transition Matrix. Create a Markov chain object from a randomly generated, right-stochastic ... Simulate Random Walks Through Markov Chain. This example shows how to generate and visualize random walks through a Markov ... Create the Markov chain that is characterized by the transition matrix P. ... Plot a directed graph of the Markov chain and identify classes using node color and markers. ...
*  Markov Chains - Recurrence
... Hi, I was reading about Markov chains in wikipedia and I've got a doubt on this topic: Markov chain ... Hi, I was reading about Markov chains in wikipedia and I've got a doubt on this topic: Markov chain - Wikipedia, the free ... The most simple example of a null-recurrent Markov chain is the symmetric random walk on $\displaystyle \mathbb{Z}$: it is ... Since $\displaystyle p_{21},0$, if the state 2 is visited infinitely often, the Markov chain will also visit the state 1 ...
*  Markov Chains and Invariant Probabilities | Onesimo Hernandez-Lerma | Springer
This book concerns discrete-time homogeneous Markov chains that admit an invariant probability measure. The main objective is ... Markov Chains and Invariant Probabilities. Authors: Hernandez-Lerma, Onesimo, Lasserre, Jean B. ... This book concerns discrete-time homogeneous Markov chains that admit an invariant probability measure. The main objective is ... self-contained presentation on some key issues about the ergodic behavior of that class of Markov chains. These issues include ...
*  Monotone dependence in graphical models for multivariate Markov chains
... and the dependence of an univariate component of the chain on its parents-according to the graph terminology-is described in ... We show that a deeper insight into the relations among marginal processes of a multivariate Markov chain can be gained by ... "Alternative Markov Properties for Chain Graphs," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics; ... "Monotone dependence in graphical models for multivariate Markov chains," Metrika: International Journal for Theoretical and ...
*  Markov Chain Monte Carlo - Sampling Methods | Coursera
And a Markov chain defines a probabilistic transition model which, given that I'm at a given state, x tells me how likely I am ... Markov Chain Monte Carlo. To view this video please enable JavaScript, and consider upgrading to a web browser that supports ... a Markov chain is defined over a state space which we are going to use x's to ... Most commonly used among these is the class of Markov Chain Monte Carlo (MCMC) algorithms, which includes the simple Gibbs ...
*  Stochastic Processes : Poisson Process and Markov Chains
a) Find the transition probabilities of the Markov chain.. (b) Find the stationary distribution of the Markov chain.. (c) ... Stochastic Processes : Poisson Process and Markov Chains. Add. Remove. 1. Suppose that shocks occur according to a Poisson ... Define X7, to be the number of white balls in the first urn at time n. Then {X0,X1,X2,.. .} is a Markov chain.. ( ... Poisson Processes and Markov Chains are investigated. The solution is detailed and well presented. The response received a ...
*  Markov Chain == Dynamic Bayesian Network? - Artificial Intelligence -
Markov Chain == Dynamic Bayesian Network? By phi , August 17, 2008. in Artificial Intelligence ... 1 I've been looking into Markov chains and understand some of the maths and probability side. However, I've noticed in many ...
*  Metropolitan/non-metropolitan divergence: A spatial Markov chain approach
Use of a spatial Markov approach shows that non-metropolitan neighbours of metropolitan regions have tended to converge during ... "The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics," Journal ... "Specification and Testing of Markov Chain Models: An Application to Convergence in the European Union," Oxford Bulletin of ... Keywords: Distribution dynamics; convergence; spatial Markov chain; metropolitan; non-metropolitan; Other versions of this item ...
*  Bibliography‣ Reversible Markov Chains and Random Walks on Graphs
spectrum for Markov chains and Markov proceses. Trans. Amer. Math. Soc. 309, pp. 557-580. Cited by: 4.7. ... 270] J.R. Norris(1997) Markov chains. Cambridge University Press. Cited by: 1.2.3, 12.2, 13.1.1, 2.1.1, 2.1.2, 2.1, 2.1, 2.9, ... 215] J.G. Kemeny and J.L. Snell(1960) Finite markov chains. Van Nostrand. Cited by: 1.2.3, 2.10.2, 2.9, 2.9, 2.9, 2.9. ... 240] L. Lovász and P. Winkler(1995) Efficient stopping rules for Markov chains. pp. 76-82. Cited by: 2.10.1, 2.9, 4.7, 9.3.1, ...
*  Markov Chain Transition Probabilities Help.
For a project I am using a Markov Chain model with 17 states. I have used data to estimate transition probabilities. From these ... Markov Chain Transition Probabilities Help. Hi. For a project I am using a Markov Chain model with 17 states. I have used data ... Re: Markov Chain Transition Probabilities Help. Hi. First you form a matrix P (dimensions k x k where k is the number of ... Re: Markov Chain Transition Probabilities Help. That is exactly what I was looking for! Thanks so much. ...
*  Statistics - Discrete Markov Chains | Physics Forums - The Fusion of Science and Community
Well for b) I got .21 and believed that I solved the problem correctly. I don't know exactly what c is even asking me. Find P(X_1 = 0). What exactly are the alphas? Like what do they represent? Alpha 1 = probability x equals zero is .25 ...
*  Infinite-State Verification: From Transition Systems to Markov Chains - IEEE Conference Publication
We present a general framework which can handle probabilistic versions of several classical models such as Petri nets, lossy channel systems, push-down aut
* : Adaptive Monte-Carlo Markov Chain
Adaptive Monte-Carlo Markov Chain. Add to your list(s) Download to your calendar using vCal ... University of Cambridge , , Isaac Newton Institute Seminar Series , Adaptive Monte-Carlo Markov Chain ...
*  Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation
Such interactions are modeled in turn by means of a set of Markov chains, one for each direction, whose parameters are ... the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, ... Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation G. Scarpa 1 R. Gaetano 1 M. Haindl 2 J. Zerubia ... Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation. IEEE Trans. on Image Processing, IEEE, 2009, 18 ...
*  Hidden Markov models, Markov chains in random environments, and systems theory | Math
Hidden Markov models, Markov chains in random environments, and systems theory Hidden Markov models, Markov chains in random ... Hidden Markov models, Markov chains in random environments, and systems theory February 6, 2008 - 11:00. - February 6, 2008 - ... weakly ergodic signals with nondegenerate observations by exploiting a surprising connection with the theory of Markov chains ... An essential ingredient of the statistical inference theory for hidden Markov models is the nonlinear filter. The asymptotic ...
*  A problem on Markov Chains | Physics Forums - The Fusion of Science and Community
Similar Discussions: A problem on Markov Chains * Markov chain problem (Replies: 11) ... Markov chains primarily have to have valid probabilities and then need to satisfy 1st order conditional dependence.. So the ... I'm stuck at a problem on markov chains... Could anyone help?. Here it is:. There are two machines that operate or don't during ...
*  Reversible-Jump Markov Chain Monte Carlo for Quantitative Trait Loci Mapping | Genetics
Gilks, W. R., S. Richardson and D. J. Spiegelhalter, 1996 Introducing Markov Chain Monte Carlo, pp. 1-19 in Markov Chain Monte ... Reversible-Jump Markov Chain Monte Carlo for Quantitative Trait Loci Mapping Message Subject (Your Name) has forwarded a page ... Green, P. J., 1995 Reversible jump Markov chain Monte carlo computation and Bayesian model determination. Biometrika 82: 711- ... Stephens, D. A., and R. D. Fisch, 1998 Bayesian analysis of quantitative trait locus data using reversible jump Markov chain ...
*  Dienekes' Anthropology Blog: 02/2013
We then used a likelihood-based Markov chain Monte Carlo procedure to estimate the most probable times in years separating ...

Vladimir Andreevich Markov: Vladimir Andreevich Markov (; May 8, 1871 – January 18, 1897) was a Russian mathematician, known for proving the Markov brothers' inequality with his older brother Andrey Markov. He died of tuberculosis at the age of 25.Monte Carlo methods for option pricing: In mathematical finance, a Monte Carlo option model uses Monte Carlo methods Although the term 'Monte Carlo method' was coined by Stanislaw Ulam in the 1940s, some trace such methods to the 18th century French naturalist Buffon, and a question he asked about the results of dropping a needle randomly on a striped floor or table. See Buffon's needle.Hyperparameter: In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis.Clonal Selection Algorithm: In artificial immune systems, Clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation.Inverse probability weighting: Inverse probability weighting is a statistical technique for calculating statistics standardized to a population different from that in which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application.Interval boundary element method: Interval boundary element method is classical boundary element method with the interval parameters.
Decoding methods: In coding theory, decoding is the process of translating received messages into codewords of a given code. There have been many common methods of mapping messages to codewords.Doob decomposition theorem: In the theory of stochastic processes in discrete time, a part of the mathematical theory of probability, the Doob decomposition theorem gives a unique decomposition of every adapted and integrable stochastic process as the sum of a martingale and a predictable process (or "drift") starting at zero. The theorem was proved by and is named for Joseph L.Branching order of bacterial phyla (Gupta, 2001): There are several models of the Branching order of bacterial phyla, one of these was proposed in 2001 by Gupta based on conserved indels or protein, termed "protein signatures", an alternative approach to molecular phylogeny. Some problematic exceptions and conflicts are present to these conserved indels, however, they are in agreement with several groupings of classes and phyla.Mac OS X Server 1.0Atomic heraldry: Atomic heraldry is heraldry characterised by the appearance of charges including the atom or showing the motion of parts of the atom; more loosely, it may describe heraldry in which atoms or the component parts thereof are represented through a combination of other charges. Obviously, this is a late development in heraldry.Negative probability: The probability of the outcome of an experiment is never negative, but quasiprobability distributions can be defined that allow a negative probability for some events. These distributions may apply to unobservable events or conditional probabilities.Matrix model: == Mathematics and physics ==Molecular evolution: Molecular evolution is a change in the sequence composition of cellular molecules such as DNA, RNA, and proteins across generations. The field of molecular evolution uses principles of evolutionary biology and population genetics to explain patterns in these changes.PSI Protein Classifier: PSI Protein Classifier is a program generalizing the results of both successive and independent iterations of the PSI-BLAST program. PSI Protein Classifier determines belonging of the found by PSI-BLAST proteins to the known families.Chromosome regionsDNA sequencer: A DNA sequencer is a scientific instrument used to automate the DNA sequencing process. Given a sample of DNA, a DNA sequencer is used to determine the order of the four bases: G (guanine), C (cytosine), A (adenine) and T (thymine).CS-BLASTVon Neumann regular ring: In mathematics, a von Neumann regular ring is a ring R such that for every a in R there exists an x in R such that . To avoid the possible confusion with the regular rings and regular local rings of commutative algebra (which are unrelated notions), von Neumann regular rings are also called absolutely flat rings, because these rings are characterized by the fact that every left module is flat.A-scan ultrasound biometry: A-scan ultrasound biometry, commonly referred to as an A-scan, is routine type of diagnostic test used in ophthalmology. The A-scan provides data on the length of the eye, which is a major determinant in common sight disorders.Biostatistics (journal): Biostatistics is a peer-reviewed scientific journal covering biostatistics, that is, statistics for biological and medical research. The journals that had cited Biostatistics the most by 2008Journal Citation Reports 2008, Science Edition were Biometrics, Journal of the American Statistical Association, Biometrika, Statistics in Medicine, and Journal of the Royal Statistical Society, Series B.Panmixia: Panmixia (or panmixis) means random mating.King C and Stanfield W.Thermal cyclerColes PhillipsDisease burden: Disease burden is the impact of a health problem as measured by financial cost, mortality, morbidity, or other indicators. It is often quantified in terms of quality-adjusted life years (QALYs) or disability-adjusted life years (DALYs), both of which quantify the number of years lost due to disease (YLDs).Incremental cost-effectiveness ratio: The incremental cost-effectiveness ratio (ICER) is a statistic used in cost-effectiveness analysis to summarise the cost-effectiveness of a health care intervention. It is defined by the difference in cost between two possible interventions, divided by the difference in their effect.Matrix population models: Population models are used in population ecology to model the dynamics of wildlife or human populations. Matrix population models are a specific type of population model that uses matrix algebra.Protein subcellular localization prediction: Protein subcellular localization prediction (or just protein localization prediction) involves the computational prediction of where a protein resides in a cell.Symmetry element: A symmetry element is a point of reference about which symmetry operations can take place. In particular, symmetry elements can be centers of inversion, axes of rotation and mirror planes.Genetic linkage: Genetic linkage is the tendency of alleles that are located close together on a chromosome to be inherited together during the meiosis phase of sexual reproduction. Genes whose loci are nearer to each other are less likely to be separated onto different chromatids during chromosomal crossover, and are therefore said to be genetically linked.Glossary of scientific names: A glossary of the meaning of scientific names of living things, viruses and prions .Threshold host density: Threshold host density (NT), in the context of wildlife disease ecology, refers to the concentration of a population of a particular organism as it relates to disease. Specifically, the threshold host density (NT) of a species refers to the minimum concentration of individuals necessary to sustain a given disease within a population.Generalizability theory: Generalizability theory, or G Theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.Mexican International Conference on Artificial Intelligence: MICAI (short for Mexican International Conference on Artificial Intelligence) is the name of an annual conference covering all areas of Artificial Intelligence (AI), held in Mexico. The first MICAI conference was held in 2000.STO-nG basis sets: STO-nG basis sets are minimal basis sets, where n primitive Gaussian orbitals are fitted to a single Slater-type orbital (STO). n originally took the values 2 - 6.Pedigree chart: A pedigree chart is a diagram that shows the occurrence and appearance or phenotypes of a particular gene or organism and its ancestors from one generation to the next,pedigree chart Genealogy Glossary -, a part of The New York Times Company.Genetic variation: right|thumbInfinite alleles model: The infinite alleles model is a mathematical model for calculating genetic mutations. The Japanese geneticist Motoo Kimura and American geneticist James F.

(1/3175) Genome-wide bioinformatic and molecular analysis of introns in Saccharomyces cerevisiae.

Introns have typically been discovered in an ad hoc fashion: introns are found as a gene is characterized for other reasons. As complete eukaryotic genome sequences become available, better methods for predicting RNA processing signals in raw sequence will be necessary in order to discover genes and predict their expression. Here we present a catalog of 228 yeast introns, arrived at through a combination of bioinformatic and molecular analysis. Introns annotated in the Saccharomyces Genome Database (SGD) were evaluated, questionable introns were removed after failing a test for splicing in vivo, and known introns absent from the SGD annotation were added. A novel branchpoint sequence, AAUUAAC, was identified within an annotated intron that lacks a six-of-seven match to the highly conserved branchpoint consensus UACUAAC. Analysis of the database corroborates many conclusions about pre-mRNA substrate requirements for splicing derived from experimental studies, but indicates that splicing in yeast may not be as rigidly determined by splice-site conservation as had previously been thought. Using this database and a molecular technique that directly displays the lariat intron products of spliced transcripts (intron display), we suggest that the current set of 228 introns is still not complete, and that additional intron-containing genes remain to be discovered in yeast. The database can be accessed at o/yeast_introns.html.  (+info)

(2/3175) Economic consequences of the progression of rheumatoid arthritis in Sweden.

OBJECTIVE: To develop a simulation model for analysis of the cost-effectiveness of treatments that affect the progression of rheumatoid arthritis (RA). METHODS: The Markov model was developed on the basis of a Swedish cohort of 116 patients with early RA who were followed up for 5 years. The majority of patients had American College of Rheumatology (ACR) functional class II disease, and Markov states indicating disease severity were defined based on Health Assessment Questionnaire (HAQ) scores. Costs were calculated from data on resource utilization and patients' work capacity. Utilities (preference weights for health states) were assessed using the EQ-5D (EuroQol) questionnaire. Hypothetical treatment interventions were simulated to illustrate the model. RESULTS: The cohort distribution among the 6 Markov states clearly showed the progression of the disease over 5 years of followup. Costs increased with increasing severity of the Markov states, and total costs over 5 years were higher for patients who were in more severe Markov states at diagnosis. Utilities correlated well with the Markov states, and the EQ-5D was able to discriminate between patients with different HAQ scores within ACR functional class II. CONCLUSION: The Markov model was able to assess disease progression and costs in RA. The model can therefore be a useful tool in calculating the cost-effectiveness of different interventions aimed at changing the progression of the disease.  (+info)

(3/3175) Multipoint oligogenic analysis of age-at-onset data with applications to Alzheimer disease pedigrees.

It is usually difficult to localize genes that cause diseases with late ages at onset. These diseases frequently exhibit complex modes of inheritance, and only recent generations are available to be genotyped and phenotyped. In this situation, multipoint analysis using traditional exact linkage analysis methods, with many markers and full pedigree information, is a computationally intractable problem. Fortunately, Monte Carlo Markov chain sampling provides a tool to address this issue. By treating age at onset as a right-censored quantitative trait, we expand the methods used by Heath (1997) and illustrate them using an Alzheimer disease (AD) data set. This approach estimates the number, sizes, allele frequencies, and positions of quantitative trait loci (QTLs). In this simultaneous multipoint linkage and segregation analysis method, the QTLs are assumed to be diallelic and to interact additively. In the AD data set, we were able to localize correctly, quickly, and accurately two known genes, despite the existence of substantial genetic heterogeneity, thus demonstrating the great promise of these methods for the dissection of late-onset oligogenic diseases.  (+info)

(4/3175) Machine learning approaches for the prediction of signal peptides and other protein sorting signals.

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently, the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, have made it possible to achieve a level of reliability where practical use in, for example automatic database annotation is feasible. In this review, we concentrate on the present status and future perspectives of SignalP, our neural network-based method for prediction of the most well-known sorting signal: the secretory signal peptide. We discuss the problems associated with the use of SignalP on genomic sequences, showing that signal peptide prediction will improve further if integrated with predictions of start codons and transmembrane helices. As a step towards this goal, a hidden Markov model version of SignalP has been developed, making it possible to discriminate between cleaved signal peptides and uncleaved signal anchors. Furthermore, we show how SignalP can be used to characterize putative signal peptides from an archaeon, Methanococcus jannaschii. Finally, we briefly review a few methods for predicting other protein sorting signals and discuss the future of protein sorting prediction in general.  (+info)

(5/3175) Genome-wide linkage analyses of systolic blood pressure using highly discordant siblings.

BACKGROUND: Elevated blood pressure is a risk factor for cardiovascular, cerebrovascular, and renal diseases. Complex mechanisms of blood pressure regulation pose a challenge to identifying genetic factors that influence interindividual blood pressure variation in the population at large. METHODS AND RESULTS: We performed a genome-wide linkage analysis of systolic blood pressure in humans using an efficient, highly discordant, full-sibling design. We identified 4 regions of the human genome that show statistical significant linkage to genes that influence interindividual systolic blood pressure variation (2p22.1 to 2p21, 5q33.3 to 5q34, 6q23.1 to 6q24.1, and 15q25.1 to 15q26.1). These regions contain a number of candidate genes that are involved in physiological mechanisms of blood pressure regulation. CONCLUSIONS: These results provide both novel information about genome regions in humans that influence interindividual blood pressure variation and a basis for identifying the contributing genes. Identification of the functional mutations in these genes may uncover novel mechanisms for blood pressure regulation and suggest new therapies and prevention strategies.  (+info)

(6/3175) FORESST: fold recognition from secondary structure predictions of proteins.

MOTIVATION: A method for recognizing the three-dimensional fold from the protein amino acid sequence based on a combination of hidden Markov models (HMMs) and secondary structure prediction was recently developed for proteins in the Mainly-Alpha structural class. Here, this methodology is extended to Mainly-Beta and Alpha-Beta class proteins. Compared to other fold recognition methods based on HMMs, this approach is novel in that only secondary structure information is used. Each HMM is trained from known secondary structure sequences of proteins having a similar fold. Secondary structure prediction is performed for the amino acid sequence of a query protein. The predicted fold of a query protein is the fold described by the model fitting the predicted sequence the best. RESULTS: After model cross-validation, the success rate on 44 test proteins covering the three structural classes was found to be 59%. On seven fold predictions performed prior to the publication of experimental structure, the success rate was 71%. In conclusion, this approach manages to capture important information about the fold of a protein embedded in the length and arrangement of the predicted helices, strands and coils along the polypeptide chain. When a more extensive library of HMMs representing the universe of known structural families is available (work in progress), the program will allow rapid screening of genomic databases and sequence annotation when fold similarity is not detectable from the amino acid sequence. AVAILABILITY: FORESST web server at for the library of HMMs of structural families used in this paper. FORESST web server at for a more extensive library of HMMs (work in progress). CONTACT:;;  (+info)

(7/3175) Age estimates of two common mutations causing factor XI deficiency: recent genetic drift is not necessary for elevated disease incidence among Ashkenazi Jews.

The type II and type III mutations at the FXI locus, which cause coagulation factor XI deficiency, have high frequencies in Jewish populations. The type III mutation is largely restricted to Ashkenazi Jews, but the type II mutation is observed at high frequency in both Ashkenazi and Iraqi Jews, suggesting the possibility that the mutation appeared before the separation of these communities. Here we report estimates of the ages of the type II and type III mutations, based on the observed distribution of allelic variants at a flanking microsatellite marker (D4S171). The results are consistent with a recent origin for the type III mutation but suggest that the type II mutation appeared >120 generations ago. This finding demonstrates that the high frequency of the type II mutation among Jews is independent of the demographic upheavals among Ashkenazi Jews in the 16th and 17th centuries.  (+info)

(8/3175) Does over-the-counter nicotine replacement therapy improve smokers' life expectancy?

OBJECTIVE: To determine the public health benefits of making nicotine replacement therapy available without prescription, in terms of number of quitters and life expectancy. DESIGN: A decision-analytic model was developed to compare the policy of over-the-counter (OTC) availability of nicotine replacement therapy with that of prescription ([symbol: see text]) availability for the adult smoking population in the United States. MAIN OUTCOME MEASURES: Long-term (six-month) quit rates, life expectancy, and smoking attributable mortality (SAM) rates. RESULTS: OTC availability of nicotine replacement therapy would result in 91,151 additional successful quitters over a six-month period, and a cumulative total of approximately 1.7 million additional quitters over 25 years. All-cause SAM would decrease by 348 deaths per year and 2940 deaths per year at six months and five years, respectively. Relative to [symbol: see text] nicotine replacement therapy availability, OTC availability would result in an average gain in life expectancy across the entire adult smoking population of 0.196 years per smoker. In sensitivity analyses, the benefits of OTC availability were evident across a wide range of changes in baseline parameters. CONCLUSIONS: Compared with [symbol: see text] availability of nicotine replacement therapy, OTC availability would result in more successful quitters, fewer smoking-attributable deaths, and increased life expectancy for current smokers.  (+info)

  • ergodic
  • The main objective is to give a systematic, self-contained presentation on some key issues about the ergodic behavior of that class of Markov chains. (
  • In this talk, I will show that this gap can be resolved in the general setting of weakly ergodic signals with nondegenerate observations by exploiting a surprising connection with the theory of Markov chains in random environments. (
  • Springer
  • Monotone dependence in graphical models for multivariate Markov chains ," Metrika: International Journal for Theoretical and Applied Statistics , Springer, vol. 76(7), pages 873-885, October. (
  • state
  • But the same chain with a 4th state that can only be accessed by state 1 and only accesses itself would make state 1 a transient state, right? (
  • 0$, if the state 2 is visited infinitely often, the Markov chain will also visit the state 1 infinitely often (if an event has positive probability and you procede to infinitely many independent trials, it will occur infinitely often). (
  • It can thus be used for describing systems that follow a chain of linked events, where what happens next depends only on the current state of the system. (
  • While the time parameter is usually discrete, the state space of a Markov chain does not have any generally agreed-on restrictions: the term may refer to a process on an arbitrary state space. (
  • However, many applications of Markov chains employ finite or countably infinite state spaces, which have a more straightforward statistical analysis. (
  • Here the process is a discrete-time Markov chain of order m and the transition probability to a state at the next time is a sum of functions, each depending on the next state and one of the m previous states. (
  • In the mathematical theory of probability, an absorbing Markov chain is a Markov chain in which every state can reach an absorbing state. (
  • A Markov chain is an absorbing chain if there is at least one absorbing state and it is possible to go from any state to at least one absorbing state in a finite number of steps. (
  • In an absorbing Markov chain, a state that is not absorbing is called transient. (
  • A basic property about an absorbing Markov chain is the expected number of visits to a transient state j starting from a transient state i (before being absorbed). (
  • The (i, j) entry of matrix N is the expected number of times the chain is in state j, given that the chain started in state i. (
  • Although in reality, the coin flips cease after the string "HTH" is generated, the perspective of the absorbing Markov chain is that the process has transitioned into the absorbing state representing the string "HTH" and, therefore, cannot leave. (
  • Very roughly, the theory of a quantum Markov chain resembles that of a measure-many automaton, with some important substitutions: the initial state is to be replaced by a density matrix, and the projection operators are to be replaced by positive operator valued measures. (
  • The state of the chain after a number of steps is then used as a sample of the desired distribution. (
  • In probability theory, a nearly completely decomposable (NCD) Markov chain is a Markov chain where the state-space can be partitioned in such a way that movement within a partition occurs much more frequently than movement between partitions. (
  • A Markov chain on a measurable state space is a discrete-time-homogenous Markov chain with a measurable space as state space. (
  • Graphs
  • Markov Properties for Acyclic Directed Mixed Graphs ," Scandinavian Journal of Statistics , Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 145-157. (
  • Alternative Markov Properties for Chain Graphs ," Scandinavian Journal of Statistics , Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(1), pages 33-85. (
  • Mixing
  • Mathematical theory focuses on how mixing times change as a function of the size of the structure underlying the chain. (
  • Measures
  • A distinguishing feature of the book is the emphasis on the role of expected occupation measures to study the long-run behavior of Markov chains on uncountable spaces. (
  • dependence
  • Granger noncausality and contemporaneous independence conditions are read off a mixed graph, and the dependence of an univariate component of the chain on its parents-according to the graph terminology-is described in terms of stochastic dominance criteria. (