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.Video Games: A form of interactive entertainment in which the player controls electronically generated images that appear on a video display screen. This includes video games played in the home on special machines or home computers, and those played in arcades.Game Theory: Theoretical construct used in applied mathematics to analyze certain situations in which there is an interplay between parties that may have similar, opposed, or mixed interests. In a typical game, decision-making "players," who each have their own goals, try to gain advantage over the other parties by anticipating each other's decisions; the game is finally resolved as a consequence of the players' decisions.Games, Experimental: Games designed to provide information on hypotheses, policies, procedures, or strategies.Congresses as Topic: Conferences, conventions or formal meetings usually attended by delegates representing a special field of interest.Post and Core Technique: Use of a metal casting, usually with a post in the pulp or root canal, designed to support and retain an artificial crown.Consensus Development Conferences as Topic: Presentations of summary statements representing the majority agreement of physicians, scientists, and other professionals convening for the purpose of reaching a consensus--often with findings and recommendations--on a subject of interest. The Conference, consisting of participants representing the scientific and lay viewpoints, is a significant means of evaluating current medical thought and reflects the latest advances in research for the respective field being addressed.DNA Fingerprinting: A technique for identifying individuals of a species that is based on the uniqueness of their DNA sequence. Uniqueness is determined by identifying which combination of allelic variations occur in the individual at a statistically relevant number of different loci. In forensic studies, RESTRICTION FRAGMENT LENGTH POLYMORPHISM of multiple, highly polymorphic VNTR LOCI or MICROSATELLITE REPEAT loci are analyzed. The number of loci used for the profile depends on the ALLELE FREQUENCY in the population.

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

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)

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

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)

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

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)

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

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)

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

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)

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

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: [email protected]; [email protected]; [email protected]  (+info)

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

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)

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

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)

  • We also consider generalizations of the Metropolis - Hastings independent chains or Metropolized independent sampling, and for some of these algorithms we are able to give the convergence rates and establish a lower bound for the asymptotic efficiency. (
  • Ching W, Ng MK (2006) Markov chains: models, algorithms and applications. (
  • We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. (
  • Motivated by multivariate random recurrence equations we prove a new analogue of the Key Renewal Theorem for functionals of a Markov chain with compact state space in the spirit of Kesten. (
  • Is there a way to analytically compute the recurrence time of a finite Markov process? (
  • Let Xn be an irreducible aperiodic recurrent Markov chain with countable state space I and with the mean recurrence times having second moments. (
  • In this work we study the recurrence problem for quantum Markov chains, which are quantum versions of classical Markov chains introduced by S. Gudder and described in terms of completely positive maps. (
  • A notion of monitored recurrence for quantum Markov chains is examined in association with Schur functions, which codify information on the first return to some given state or subspace. (
  • This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. (
  • In probability theory, a telescoping Markov chain (TMC) is a vector-valued stochastic process that satisfies a Markov property and admits a hierarchical format through a network of transition matrices with cascading dependence. (
  • 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. (
  • It is named after the Russian mathematician Andrey Markov . (
  • In a Markov chain (named for Russian mathematician Andrey Markov [ Figure ]), the probability of the next computed estimated outcome depends only on the current estimate and not on prior estimates. (
  • The main purpose of this work is to investigate the performance of hidden Markov (chain) models (HMMs) in comparison to hidden Markov random field (HMRF) models when predicting CT images of head. (
  • Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. (
  • For instance, I found tons of verbose material on Hidden Markov Models, but I still havent a freaking clue on what the damn thing is, because not a single time did I ever see a reference to introductory material. (
  • 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. (
  • Graphical models for multivariate Markov chains ," Journal of Multivariate Analysis , Elsevier, vol. 107(C), pages 90-103. (
  • Markov chain Monte Carlo methods) to calibrate micro-simulation models. (
  • The discrete probability models are represented by Markov process, which is based on the concept of probabilistic cumulative damage [ 8 ] and now commonly used in performance prediction of infrastructure facilities [ 9 ]. (
  • Methods of supplementary variables [ 14 ] and the device of stages [ 15 ] are two classical approaches of extended Markov-models. (
  • Several authors have studied the relationship between hidden Markov models and "Boltzmann chains" with a linear or "time-sliced" architecture. (
  • An essential ingredient of the statistical inference theory for hidden Markov models is the nonlinear filter. (
  • Reversible jump Markov chain Monte Carlo methods are used to implement a sampling scheme in which the Markov chain can jump between parameter subspaces corresponding to models with different numbers of quantitative-trait loci (QTL's). (
  • Nicolis J.S., Protonotarios E.N., Voulodemou I. (1978) Controlled Markov Chain Models for Biological Hierarchies. (
  • Models for the extremes of Markov chains. (
  • In this paper, we focus on Markov chains, deriving a class of models for their joint tail which allows the degree of clustering of extremes to decrease at high levels, overcoming a key Limitation in current methodologies. (
  • Markov Decision Process (MDP) models have been widely used in decision making under uncertainty. (
  • This has usually been done with regression models, but Markov chain methods have also been applied. (
  • However, many applications of Markov chains employ finite or countably infinite state spaces, which have a more straightforward statistical analysis. (
  • 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. (
  • j A k Thus (1.14) states the chain allows no communication between the subsets A 0 and A 1 of F. Hence we may reduce the original chain to independent chains on the reduced state spaces A 0, A 1. (
  • A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. (
  • In other words, a Markov chain is able to improve its approximation to the true distribution at each step in the simulation. (
  • Addendum: Here is a simulation of 100,000 steps of the chain using R software, where state 0 = Sun and state 1 = Rain. (
  • total current, sodium current, potassium current, a timetrack vector that is in seconds, a sodium matrix showing the number of channels in each Markov state, a potassium matrix showing the number of channels in each Markov state, the total number of sodium channels, the total number of potassium channels, and the time it took the simulation to run. (
  • trigamma( α )- 1 /λ- 1 /λ α/λ 2 = α trigamma( α )- 1 λ 2 and the Jeffreys prior is g ( α,λ ) = p α trigamma( α )- 1 λ 2 2 The Markov Chain Monte Carlo 2.1 Ordinary Monte Carlo The "Monte Carlo method" refers to the theory and practice of learning about probability distributions by simulation rather than calculus. (
  • Asymptotic study of an estimator of the entropy rate of a two-state Markov chain for one long trajectory. (
  • We introduce an estimate of the entropy $\mathbb{E}_{p^t}(\log p^t)$ of the marginal density p t of a (eventually inhomogeneous) Markov chain at time t=1. (
  • The rare ebook circulation distribution entropy production and irreversibility of denumerable markov chains is powered into payment with the spatial tracking by using the exclusive mushrooms. (
  • we shall formalize different interpretations as different mixing times , and relations between mixing times are discussed in Chapter 4 for reversible chains and in Chapter 8 (xxx section to be written) for general chains. (
  • A chain satisfying a detailed balance relation $\pi(x) P(x,y) =\pi(y) P(y,x)$ is reversible . (
  • 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. (
  • Markov Chain Monte Carlo to sample from probability distributions is a good start - if you are into sampling. (
  • Markov chain Monte Carlo simulations allow researchers to approximate posterior distributions that cannot be directly calculated. (
  • We consider various scenarios where shepherding distributions can be used, including the case where several machines or CPU cores work on the same data in parallel (the so-called transition parallel application of the framework) and the case where a large data set itself can be partitioned across several machines or CPU cores and various chains work on subsets of the data (the so-called data parallel application of the framework). (
  • This cyclostationary Markov-chain approach captures the spring barrier in ENSO predictability and gives insight into the dependence of ENSO predictability on the climatic state. (