Case Studies in Bayesian Statistical Modelling and Analysis by Walter A. Shewhart, Samuel S. Wilks(eds.)

By Walter A. Shewhart, Samuel S. Wilks(eds.)

This publication goals to give an advent to Bayesian modelling and computation, through contemplating actual case reports drawn from various fields spanning ecology, healthiness, genetics and finance. each one bankruptcy includes an outline of the matter, the corresponding version, the computational approach, effects and inferences in addition to the problems that come up within the implementation of those ways.

Case reports in Bayesian Statistical Modelling and Analysis:

  • Illustrates tips to do Bayesian research in a transparent and concise demeanour utilizing real-world difficulties.
  • Each bankruptcy specializes in a real-world challenge and describes the best way the matter might be analysed utilizing Bayesian tools.
  • Features methods that may be utilized in a large region of program, reminiscent of, future health, the surroundings, genetics, info technology, medication, biology, and distant sensing.

Case reports in Bayesian Statistical Modelling and Analysis is aimed toward statisticians, researchers and practitioners who've a few services in statistical modelling and research, and a few realizing of the fundamentals of Bayesian information, yet little event in its software. Graduate scholars of statistics and biostatistics also will locate this e-book invaluable.

Chapter 1 advent (pages 1–16): Clair L. Alston, Margaret Donald, Kerrie L. Mengersen and Anthony N. Pettitt
Chapter 2 creation to MCMC (pages 17–29): Anthony N. Pettitt and Candice M. Hincksman
Chapter three Priors: Silent or lively companions of Bayesian Inference? (pages 30–65): Samantha Low Choy
Chapter four Bayesian research of the traditional Linear Regression version (pages 66–89): Christopher M. Strickland and Clair L. Alston
Chapter five Adapting ICU Mortality types for neighborhood information: A Bayesian technique (pages 90–102): Petra L. Graham, Kerrie L. Mengersen and David A. Cook
Chapter 6 A Bayesian Regression version with Variable choice for Genome?Wide organization reviews (pages 103–117): Carla Chen, Kerrie L. Mengersen, Katja Ickstadt and Jonathan M. Keith
Chapter 7 Bayesian Meta?Analysis (pages 118–140): Jegar O. Pitchforth and Kerrie L. Mengersen
Chapter eight Bayesian combined results types (pages 141–158): Clair L. Alston, Christopher M. Strickland, Kerrie L. Mengersen and Graham E. Gardner
Chapter nine Ordering of Hierarchies in Hierarchical versions: Bone Mineral Density Estimation (pages 159–170): Cathal D. Walsh and Kerrie L. Mengersen
Chapter 10 Bayesian Weibull Survival version for Gene Expression facts (pages 171–185): Sri Astuti Thamrin, James M. McGree and Kerrie L. Mengersen
Chapter eleven Bayesian switch aspect Detection in tracking scientific results (pages 186–196): Hassan Assareh, Ian Smith and Kerrie L. Mengersen
Chapter 12 Bayesian Splines (pages 197–220): Samuel Clifford and Samantha Low Choy
Chapter thirteen affliction Mapping utilizing Bayesian Hierarchical versions (pages 221–239): Arul Earnest, Susanna M. Cramb and Nicole M. White
Chapter 14 Moisture, vegetation and Salination: An research of a Three?Dimensional Agricultural info Set (pages 240–251): Margaret Donald, Clair L. Alston, Rick younger and Kerrie L. Mengersen
Chapter 15 A Bayesian method of Multivariate nation area Modelling: A learn of a Fama–French Asset?Pricing version with Time?Varying Regressors (pages 252–266): Christopher M. Strickland and Philip Gharghori
Chapter sixteen Bayesian mix types: whilst the item you must recognize is the object you can't degree (pages 267–286): Clair L. Alston, Kerrie L. Mengersen and Graham E. Gardner
Chapter 17 Latent classification types in drugs (pages 287–309): Margaret Rolfe, Nicole M. White and Carla Chen
Chapter 18 Hidden Markov types for complicated Stochastic approaches: A Case learn in Electrophysiology (pages 310–329): Nicole M. White, Helen Johnson, Peter Silburn, Judith Rousseau and Kerrie L. Mengersen
Chapter 19 Bayesian category and Regression bushes (pages 330–347): Rebecca A. O'Leary, Samantha Low Choy, Wenbiao Hu and Kerrie L. Mengersen
Chapter 20 Tangled Webs: utilizing Bayesian Networks within the struggle opposed to an infection (pages 348–360): Mary Waterhouse and Sandra Johnson
Chapter 21 enforcing Adaptive dose discovering reports utilizing Sequential Monte Carlo (pages 361–373): James M. McGree, Christopher C. Drovandi and Anthony N. Pettitt
Chapter 22 Likelihood?Free Inference for Transmission premiums of Nosocomial Pathogens (pages 374–387): Christopher C. Drovandi and Anthony N. Pettitt
Chapter 23 Variational Bayesian Inference for combination versions (pages 388–402): Clare A. McGrory
Chapter 24 matters in Designing Hybrid Algorithms (pages 403–420): Jeong E. Lee, Kerrie L. Mengersen and Christian P. Robert
Chapter 25 A Python package deal for Bayesian Estimation utilizing Markov Chain Monte Carlo (pages 421–460): Christopher M. Strickland, Robert J. Denham, Clair L. Alston and Kerrie L. Mengersen

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Journal of the American Statistical Association 103(3), 248–258. Jasra A, Stephens D and Holmes C 2007a On population based simulation for statistical inference. Statistics and Computing 17(3), 263–279. Jasra A, Stephens D and Holmes C 2007b Population-based reversible jump Markov chain Monte Carlo. Biometrika 94(4), 787–807. Kirkpatrick S, Gelatt C and Vecchi M 1983 Optimization by simulated annealing. Science 220(3), 671–680. Lunn DJ, Thomas A, Best N and Spiegelhalter D 2000 WinBUGS – A Bayesian modelling framework: concepts, structure, and extensibility.

The text then focuses on convergence diagnostics, largely grouped into those based on graphical plots, stopping rules and confidence bounds. The approaches are illustrated through benchmark examples and case studies. The second book, by Robert and Casella, Monte Carlo Statistical Methods (Robert and Casella 1999, 2004), commences with an introduction (statistical models, likelihood methods, Bayesian methods, deterministic numerical methods, prior distributions and bootstrap methods), then covers random variable generation, Monte Carlo approaches (integration, variance, optimization), Markov chains, popular algorithms (Metropolis–Hastings, slice sampler, two-stage and multi-stage Gibbs, variable selection, reversible jump, perfect sampling, iterated and sequential importance sampling) and convergence.

Statistics and Computing 8, 319–335. Brooks SP, Friel N and King R 2003 Classical model selection via simulated annealing. Journal of the Royal Statistical Society, Series B 65(3), 503–520. Caimo A and Friel N 2011 Bayesian inference for exponential random graph models. Social Networks 33(3), 41–55. Carlin B, Gelfand A and Smith A 1992 Hierarchical Bayesian analysis of changepoint problems. Applied Statistics 41, 389–405. Chopin N 2002 A sequential particle filter method for static models. Biometrika 89(3), 539–551.

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Case Studies in Bayesian Statistical Modelling and Analysis by Walter A. Shewhart, Samuel S. Wilks(eds.)
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