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Baysian Decision Problems And Markov Chains
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Book Synopsis Bayesian Decision Problems and Markov Chains by : James John Martin
Download or read book Bayesian Decision Problems and Markov Chains written by James John Martin and published by . This book was released on 1967 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book ... deals with a theoretical foundation for the solution of decision problems in a Markov chain with uncertain transition probabilities and considers both sequential sampling and fixed-sample-size problems." -- Preface.
Book Synopsis BAYESIAN DECISION PROBLEMS AND MARKOV CHAINS by : James J. Martin
Download or read book BAYESIAN DECISION PROBLEMS AND MARKOV CHAINS written by James J. Martin and published by . This book was released on 1975 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Decision Problems and Markov Chains by : Juan José Martín González
Download or read book Bayesian Decision Problems and Markov Chains written by Juan José Martín González and published by . This book was released on 1967 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Some Bayesian Decision Problems in a Markov Chain by : James John Martin
Download or read book Some Bayesian Decision Problems in a Markov Chain written by James John Martin and published by . This book was released on 1965 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Decision Problems and Markov Chains by : James John Martin
Download or read book Bayesian Decision Problems and Markov Chains written by James John Martin and published by . This book was released on 1967 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book ... deals with a theoretical foundation for the solution of decision problems in a Markov chain with uncertain transition probabilities and considers both sequential sampling and fixed-sample-size problems." -- Preface.
Book Synopsis Bayesian Decision Making and Learning for Continuous-time Markov Systems by : Erdal Panayirci
Download or read book Bayesian Decision Making and Learning for Continuous-time Markov Systems written by Erdal Panayirci and published by . This book was released on 1970 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: The document is concerned with Bayesian decision making and learning algorithms for a particular problem in parametric pattern recognition in which each of a finite set of pattern classes is characterized by a continuous-time, discrete-state Markov process. The basic problem considered is that of determining rules for making decisions about the identity of the active pattern class based upon observation of a sample function in some finite interval. The stationary transition probability matrices for the processes in question are the parameters of the pattern classes. (Author).
Book Synopsis Admissibility of Formal Bayes Inferences in Quadratically Regular Decision Problems by : Wen-Lin Lai
Download or read book Admissibility of Formal Bayes Inferences in Quadratically Regular Decision Problems written by Wen-Lin Lai and published by . This book was released on 1996 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Networks and Decision Graphs by : Thomas Dyhre Nielsen
Download or read book Bayesian Networks and Decision Graphs written by Thomas Dyhre Nielsen and published by Springer Science & Business Media. This book was released on 2009-03-17 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.
Book Synopsis Bayesian Decision Problems and Marcov Chains by : James J. Martin
Download or read book Bayesian Decision Problems and Marcov Chains written by James J. Martin and published by . This book was released on 1975 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Decision Theory Applied to the Finite State Markov Decision Problem by : William Ross Osgood
Download or read book Bayesian Decision Theory Applied to the Finite State Markov Decision Problem written by William Ross Osgood and published by . This book was released on 1971 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Analysis of Stochastic Process Models by : David Insua
Download or read book Bayesian Analysis of Stochastic Process Models written by David Insua and published by John Wiley & Sons. This book was released on 2012-05-07 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.
Book Synopsis Bayesian Decision Theory Applied to the Finite State Markov Decision Problem by : William Ross Osgood
Download or read book Bayesian Decision Theory Applied to the Finite State Markov Decision Problem written by William Ross Osgood and published by . This book was released on 1971 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Markov Chain Monte Carlo by : Dani Gamerman
Download or read book Markov Chain Monte Carlo written by Dani Gamerman and published by CRC Press. This book was released on 2006-05-10 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.
Book Synopsis Markov Decision Processes and Stochastic Positional Games by : Dmitrii Lozovanu
Download or read book Markov Decision Processes and Stochastic Positional Games written by Dmitrii Lozovanu and published by Springer Nature. This book was released on 2024-02-13 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent findings and results concerning the solutions of especially finite state-space Markov decision problems and determining Nash equilibria for related stochastic games with average and total expected discounted reward payoffs. In addition, it focuses on a new class of stochastic games: stochastic positional games that extend and generalize the classic deterministic positional games. It presents new algorithmic results on the suitable implementation of quasi-monotonic programming techniques. Moreover, the book presents applications of positional games within a class of multi-objective discrete control problems and hierarchical control problems on networks. Given its scope, the book will benefit all researchers and graduate students who are interested in Markov theory, control theory, optimization and games.
Book Synopsis Markov Decision Problems with Countable State Spaces by : H. M. Dietz
Download or read book Markov Decision Problems with Countable State Spaces written by H. M. Dietz and published by Walter de Gruyter GmbH & Co KG. This book was released on 1984-01-14 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: No detailed description available for "Markov Decision Problems with Countable State Spaces".
Book Synopsis Bayesian Inference for Stochastic Processes by : Lyle D. Broemeling
Download or read book Bayesian Inference for Stochastic Processes written by Lyle D. Broemeling and published by CRC Press. This book was released on 2017-12-12 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.
Book Synopsis Recent Developments in Markov Decision Processes by : Roger Hartley
Download or read book Recent Developments in Markov Decision Processes written by Roger Hartley and published by . This book was released on 1980 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:
