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Bayesian Decision Problems And Marcov 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 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 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 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 Finite Markov Processes and Their Applications by : Marius Iosifescu
Download or read book Finite Markov Processes and Their Applications written by Marius Iosifescu and published by Courier Corporation. This book was released on 2014-07-01 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained treatment of finite Markov chains and processes, this text covers both theory and applications. Author Marius Iosifescu, vice president of the Romanian Academy and director of its Center for Mathematical Statistics, begins with a review of relevant aspects of probability theory and linear algebra. Experienced readers may start with the second chapter, a treatment of fundamental concepts of homogeneous finite Markov chain theory that offers examples of applicable models. The text advances to studies of two basic types of homogeneous finite Markov chains: absorbing and ergodic chains. A complete study of the general properties of homogeneous chains follows. Succeeding chapters examine the fundamental role of homogeneous infinite Markov chains in mathematical modeling employed in the fields of psychology and genetics; the basics of nonhomogeneous finite Markov chain theory; and a study of Markovian dependence in continuous time, which constitutes an elementary introduction to the study of continuous parameter stochastic processes.
Book Synopsis University of Michigan Official Publication by : University of Michigan
Download or read book University of Michigan Official Publication written by University of Michigan and published by UM Libraries. This book was released on 1984 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Each number is the catalogue of a specific school or college of the University.
Book Synopsis College of Engineering by : University of Michigan. College of Engineering
Download or read book College of Engineering written by University of Michigan. College of Engineering and published by UM Libraries. This book was released on 1992 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Case Studies in Bayesian Statistical Modelling and Analysis by : Clair L. Alston
Download or read book Case Studies in Bayesian Statistical Modelling and Analysis written by Clair L. Alston and published by John Wiley & Sons. This book was released on 2012-10-10 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.
Book Synopsis Adaptivity and Learning by : Reimer Kühn
Download or read book Adaptivity and Learning written by Reimer Kühn and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptivity and learning have in recent decades become a common concern of scientific disciplines. These issues have arisen in mathematics, physics, biology, informatics, economics, and other fields more or less simultaneously. The aim of this publication is the interdisciplinary discourse on the phenomenon of learning and adaptivity. Different perspectives are presented and compared to find fruitful concepts for the disciplines involved. The authors select problems showing representative traits concerning the frame up, the methods and the achievements rather than to present extended overviews.
Book Synopsis Adaptive and Learning Agents by : Peter Vrancx
Download or read book Adaptive and Learning Agents written by Peter Vrancx and published by Springer Science & Business Media. This book was released on 2012-03-09 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Adaptive and Learning Agents, ALA 2011, held at the 10th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2011, in Taipei, Taiwan, in May 2011. The 7 revised full papers presented together with 1 invited talk were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on single and multi-agent reinforcement learning, supervised multiagent learning, adaptation and learning in dynamic environments, learning trust and reputation, minority games and agent coordination.
Book Synopsis Foundations of Learning Classifier Systems by : Larry Bull
Download or read book Foundations of Learning Classifier Systems written by Larry Bull and published by Springer Science & Business Media. This book was released on 2005-07-22 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
Download or read book NBS Special Publication written by and published by . This book was released on 1970 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptive Agents and Multi-Agent Systems II by : Daniel Kudenko
Download or read book Adaptive Agents and Multi-Agent Systems II written by Daniel Kudenko and published by Springer. This book was released on 2005-02-18 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive agents and multi-agent systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, software engineering, and developmental biology, as well as cognitive and social science. This book presents 17 revised and carefully reviewed papers taken from two workshops on the topic as well as 2 invited papers by leading researchers in the area. The papers deal with various aspects of machine learning, adaptation, and evolution in the context of agent systems and autonomous agents.
Book Synopsis Optimal Learning by : Warren B. Powell
Download or read book Optimal Learning written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2013-07-09 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.
Book Synopsis An Author and Permuted Title Index to Selected Statistical Journals by : Brian L. Joiner
Download or read book An Author and Permuted Title Index to Selected Statistical Journals written by Brian L. Joiner and published by . This book was released on 1970 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: All articles, notes, queries, corrigenda, and obituaries appearing in the following journals during the indicated years are indexed: Annals of mathematical statistics, 1961-1969; Biometrics, 1965-1969#3; Biometrics, 1951-1969; Journal of the American Statistical Association, 1956-1969; Journal of the Royal Statistical Society, Series B, 1954-1969,#2; South African statistical journal, 1967-1969,#2; Technometrics, 1959-1969.--p.iv.
Author :Jean-Claude Bermond Publisher :Springer Science & Business Media ISBN 13 :9783540516873 Total Pages :328 pages Book Rating :4.5/5 (168 download)
Book Synopsis Distributed Algorithms by : Jean-Claude Bermond
Download or read book Distributed Algorithms written by Jean-Claude Bermond and published by Springer Science & Business Media. This book was released on 1989-09-06 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the papers presented at the Third International Workshop on Distributed Algorithms organized at La Colle-sur-Loup, near Nice, France, September 26-28, 1989 which followed the first two successful international workshops in Ottawa (1985) and Amsterdam (1987). This workshop provided a forum for researchers and others interested in distributed algorithms on communication networks, graphs, and decentralized systems. The aim was to present recent research results, explore directions for future research, and identify common fundamental techniques that serve as building blocks in many distributed algorithms. Papers describe original results in all areas of distributed algorithms and their applications, including: distributed combinatorial algorithms, distributed graph algorithms, distributed algorithms for control and communication, distributed database techniques, distributed algorithms for decentralized systems, fail-safe and fault-tolerant distributed algorithms, distributed optimization algorithms, routing algorithms, design of network protocols, algorithms for transaction management, composition of distributed algorithms, and analysis of distributed algorithms.
