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Bayesian Computation with R
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Jim Albert
There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free ...
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Scientific Reasoning: The Bayesian Approach
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Colin Howson
This book gives a clear comprehensive explanation and defense of the Bayesian account of scientific reasoning. It will be read not only by philosophers and theorists of scientific method but also by working scientists, uneasy about the justification of the statistical methods now in use. Since the book is designed to explain to the uninitiated the ...
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Bayesian Networks and Decision Graphs
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by
Finn B Jensen
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query ...
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Data Analysis: A Bayesian Tutorial
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D S Sivia
Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. ...
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Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers
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Thomas Leonard, John S J Hsu
This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It is unique in presenting Bayesian statistics with a practical flavor and an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data. The authors draw on ...
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Likelihood, Bayesian and MCMC Methods in Quantitative Genetics
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Daniel Sorensen, Daniel Gianola
Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to ...
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Bayesian Data Analysis, Second Edition
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Andrew Gelman, John B Carlin, Hal S Stern
Incorporating new and updated information, this second edition of the bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analysis from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and ...
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Introduction to Bayesian Statistics
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William M Bolstad
Praise for the First Edition - 'I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics'. - "Statistics in Medical Research". '[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job ...
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Rethinking the Foundations of Statistics
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Joseph B Kadane, Mark J Schervish, Teddy Seidenfeld
This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for ...
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Modeling and Reasoning with Bayesian Networks
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by
Adnan Darwiche
A thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. ...
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Bayesian Epistemology
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Luc Bovens, Stephan Hartmann
Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the ...
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Bayesian Statistics: An Introduction
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Peter M Lee
Bayesian Statistics is the school of thought that uses all information surrounding the likelihood of an event rather than just that collected experimentally. Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee's well-established introduction maintains the clarity of exposition and use of examples ...
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Stochastic processes and filtering theory
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Andrew H. Jazwinski
This book presents a unified treatment of linear and nonlinear filtering theory for engineers, with sufficient emphasis on applications to enable the reader to use the theory. The need for this book is twofold. First, although linear estimation theory is relatively well known, it is largely scattered in the journal literature and has not been ...
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Bayesian Methods: A Social and Behavioral Sciences Approach
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by
Jeff Gill
Requiring only a background in introductory statistics, calculus, and matrix algebra, "Bayesian Methods: A Social and Behavioral Sciences Approach" provides detailed explanations of derivations and theories using a computationally oriented approach. The second edition of this popular text features new updates on such topics as MCMC algorithms, ...
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Bayesian Theory
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Jos? M Bernardo, Adrian F M Smith
This text, now available in paperback, provides a thorough account of key basic concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development, which provides, in particular, a detailed treatment of the problem ...
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Bayesian Modeling Using Winbugs
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Ioannis Ntzoufras
A hands-on introduction to the principles of Bayesian modeling. Using "WinBUGS Bayesian Modeling Using WinBUGS" provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the ...
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Bayesian Survival Analysis
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Joseph George Ibrahim, Ming-Hui Chen, Debajyoti Sinha
Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non ...
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Bayesian Reliability
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Michael S Hamada, Alyson G Wilson, C Shane Reese
Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based ...
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The Bayesian Choice
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Christian P Robert
This work is the winner of the 2004 DeGroot Prize. The DeGroot Prize is awarded every two years by the International Society for Bayesian Analysis in recognition of an important, timely, thorough and notably original contribution to the statistics literature. This graduate-level textbook presents an introduction to Bayesian statistics and decision ...
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Bayesian Econometric Methods
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Gary Koop, Dale L Poirier, Justin L Tobias
A new book in the Econometric Exercises series, this volume contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics. The latter ...
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Bayesian Core: A Practical Approach to Computational Bayesian Statistics
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Jean-Michel Marin
This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, ...
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Bayesian Statistical Modelling
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by
Peter Congdon
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating ...
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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
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by
Scott M Lynch
"Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science ...
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
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by
Kevin H Knuth (Editor), Ali E Abbas (Editor), Robin D Morris (Editor)
All papers were peer-reviewed. For over 25 years the MaxEnt workshops have explored Bayesian and Maximum Entropy methods in scientific, engineering, and signal processing applications. This proceedings volume covers all aspects of probabilistic inference such as techniques, applications, and foundations. Applications include physics, space science ...
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An Introduction to Decision Theory
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by
Martin Peterson
This up-to-date introduction to decision theory offers comprehensive and accessible discussions of decision-making under ignorance and risk, the foundations of utility theory, the debate over subjective and objective probability, Bayesianism, causal decision theory, game theory, and social choice theory. No mathematical skills are assumed, and all ...
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