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Springer Series in Statistics

Monte Carlo Methods in Bayesian Computation

Authors: Chen, Ming-Hui, Shao, Qi-Man, Ibrahim, Joseph G.

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eBook 130,89 €
price for Spain (gross)
  • ISBN 978-1-4612-1276-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 171,59 €
price for Spain (gross)
  • ISBN 978-0-387-98935-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 171,59 €
price for Spain (gross)
  • ISBN 978-1-4612-7074-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

Sampling from the posterior distribution and computing posterior quanti­ ties of interest using Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced Bayesian computation. This book examines each of these issues in detail and focuses heavily on comput­ ing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo (MC) methods for estimation of posterior summaries, improv­ ing simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, Highest Poste­ rior Density (HPD) interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. Also extensive discussion is given for computations in­ volving model comparisons, including both nested and nonnested models. Marginal likelihood methods, ratios of normalizing constants, Bayes fac­ tors, the Savage-Dickey density ratio, Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), the reverse jump algorithm, and model adequacy using predictive and latent residual approaches are also discussed. The book presents an equal mixture of theory and real applications.

Reviews

"This book combines the theory topics with good computer and application examples from the field of food science, agriculture, cancer and others. The volume will provide an excellent research resource for statisticians with an interest in computer intensive methods for modelling with different sorts of prior information."
A.V. Tsukanov in "Short Book Reviews", Vol. 20/3, December 2000

Table of contents (10 chapters)

  • Introduction

    Chen, Ming-Hui (et al.)

    Pages 1-18

    Preview Buy Chapter 30,19 €
  • Markov Chain Monte Carlo Sampling

    Chen, Ming-Hui (et al.)

    Pages 19-66

    Preview Buy Chapter 30,19 €
  • Basic Monte Carlo Methods for Estimating Posterior Quantities

    Chen, Ming-Hui (et al.)

    Pages 67-93

    Preview Buy Chapter 30,19 €
  • Estimating Marginal Posterior Densities

    Chen, Ming-Hui (et al.)

    Pages 94-123

    Preview Buy Chapter 30,19 €
  • Estimating Ratios of Normalizing Constants

    Chen, Ming-Hui (et al.)

    Pages 124-190

    Preview Buy Chapter 30,19 €

Buy this book

eBook 130,89 €
price for Spain (gross)
  • ISBN 978-1-4612-1276-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 171,59 €
price for Spain (gross)
  • ISBN 978-0-387-98935-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 171,59 €
price for Spain (gross)
  • ISBN 978-1-4612-7074-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Monte Carlo Methods in Bayesian Computation
Authors
Series Title
Springer Series in Statistics
Copyright
2000
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4612-1276-8
DOI
10.1007/978-1-4612-1276-8
Hardcover ISBN
978-0-387-98935-8
Softcover ISBN
978-1-4612-7074-4
Series ISSN
0172-7397
Edition Number
1
Number of Pages
XIII, 387
Topics