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Current Trends in Bayesian Methodology with Applications

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advanced Bayesian statistical methods
Arm
Bayesian data analysis
biostatistics applications
carlo
Category=PBTB
chain
Conditional Posterior Distributions
credible
density
distribution
Dynamic State Space Models
empirical Bayes estimation
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Full Conditional Distributions
General Fuzzy Numbers
Griddy Gibbs Sampler
Ground Truth Segmentation
hierarchical models
HMC
inference
M3 M4 M5 M6 M7
markov
MCMC Algorithm
MCMC Sample
monte
Numerical Standard Error
posterior
Posterior Density
Posterior Distribution
Posterior Inclusion Probabilities
Posterior Predictive Distribution
Precision Matrix
prior
Prior Distribution
SAE
Shape Space
Small Area Estimation Techniques
SMC Algorithm
spatial modeling techniques
Subspace Clustering
SV Model
uncertainty quantification
Variable Selection

Product details

  • ISBN 9780367377625
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 07 Oct 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.

Each chapter is self-contained and focuses on a Bayesian methodology. It gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples.

This book reflects the diversity of Bayesian analysis, from novel Bayesian methodology, such as nonignorable response and factor analysis, to state-of-the-art applications in economics, astrophysics, biomedicine, oceanography, and other areas. It guides readers in using Bayesian techniques for a range of statistical analyses.
About

Satyanshu K. Upadhyay is a professor and head of the Department of Statistics at Banaras Hindu University.

Umesh Singh is a professor in the Department of Statistics at Banaras Hindu University.

Dipak K. Dey is a distinguished professor in the Department of Statistics at the University of Connecticut.

Appaia Loganathan is a professor in the Department of Statistics at Manonmaniam Sundaranar University.