Bayesian theory and applications

edited by Paul Damien ... [et al.]

The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and developments, and who may be looking for ideas that could spawn new research. Hence, the audience for this unique book would likely include academicians/practitioners, and could likely be required reading for undergraduate and graduate students in statistics, medicine, engineering, scientific computation, business, psychology, bio-informatics, computational physics, graphical models, neural networks, geosciences, and public policy. The book honours the contributions of Sir Adrian F. M. Smith, one of the seminal Bayesian researchers, with his papers on hierarchical models, sequential Monte Carlo, and Markov chain Monte Carlo and his mentoring of numerous graduate students -the chapters are authored by prominent statisticians influenced by him. Bayesian Theory and Applications should serve the dual purpose of a reference book, and a textbook in Bayesian Statistics.

「Nielsen BookData」より

[目次]

  • Introduction
  • I EXCHANGEABILITY
  • 1. Observables and Models: exchangeability and the inductive argument
  • 2. Exchangeability and its Ramifications
  • II HIERARCHICAL MODELS
  • 3. Hierarchical Modeling
  • 4. Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification
  • 5. Flexible Bayesian modelling for clustered categorical responses in developmental toxicology
  • III MARKOV CHAIN MONTE CARLO
  • 6. Markov chain Monte Carlo Methods
  • 7. Advances in Markov chain Monte Carlo
  • IV DYNAMIC MODELS
  • 8. Bayesian Dynamic Modelling
  • 9. Hierarchical modeling in time series: the factor analytic approach
  • 10. Dynamic and spatial modeling of block maxima extremes
  • V SEQUENTIAL MONTE CARLO
  • 11. Online Bayesian learning in dynamic models: An illustrative introduction to particle methods
  • 12. Semi-supervised Classification of Texts Using Particle Learning for Probabilistic Automata
  • VI NONPARAMETRICS
  • 13. Bayesian Nonparametrics
  • 14. Geometric Weight Priors and their Applications
  • 15. Revisiting Bayesian Curve Fitting Using Multivariate Normal Mixtures
  • VII SPLINE MODELS AND COPULAS
  • 16. Applications of Bayesian Smoothing Splines
  • 17. Bayesian Approaches to Copula Modelling
  • VIII MODEL ELABORATION AND PRIOR DISTRIBUTIONS
  • 18. Hypothesis Testing and Model Uncertainty
  • 19. Proper and non-informative conjugate priors for exponential family models
  • 20. Bayesian Model Specification: Heuristics and Examples
  • 21. Case studies in Bayesian screening for time-varying model structure: The partition problem
  • IX REGRESSIONS AND MODEL AVERAGING
  • 22. Bayesian Regression Structure Discovery
  • 23. Gibbs sampling for ordinary, robust and logistic regression with Laplace priors
  • 24. Bayesian Model Averaging in the M-Open Framework
  • X FINANCE AND ACTUARIAL SCIENCE
  • 25. Asset Allocation in Finance: A Bayesian Perspective
  • 26. Markov Chain Monte Carlo Methods in Corporate Finance
  • 27. Actuarial Credibity Theory and Bayesian Statistics - The Story of a Special Evolution
  • XI MEDICINE AND BIOSTATISTICS
  • 28. Bayesian Models in Biostatistics and Medicine
  • 29. Subgroup Analysis
  • 30. Surviving Fully Bayesian Nonparametric Regression Models
  • XII INVERSE PROBLEMS AND APPLICATIONS
  • 31. Inverse Problems
  • 32. Approximate marginalization over modeling errors and uncertainties in inverse problems
  • 33. Bayesian reconstruction of particle beam phase space

「Nielsen BookData」より

この本の情報

書名 Bayesian theory and applications
著作者等 Damien, Paul
Polson, Nicholas G.
Stephens David A.
Dellaportas Petros
出版元 Oxford University Press
刊行年月 2013
ページ数 xiii, 702 p.
大きさ 24 cm
ISBN 9780199695607
NCID BB11646238
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言語 英語
出版国 イギリス
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