Bayesian Methods

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A01=Jeff Gill
Assessing Model Quality
Author_Jeff Gill
BaM package in R
Bayes Factor
Bayesian computing
Bayesian decision theory
Bayesian decision theory applications
Bayesian hierarchical regression models
Bayesian inference
Bayesian Linear Model
Bayesian modeling
Bayesian Statistics for Social Scientists
Bayesian stochastic simulation
BUGS software implementation
Category=PBTB
Conjugate Prior
empirical Bayes and James-Stein estimation
empirical Bayes estimation
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Ergodic Markov Chain
Exponential Family Forms
Full Conditional Distributions
Gibbs Sampler
Hamiltonian Monte Carlo
HPD Interval
Intrinsic Bayes Factor
Jag Package
James Stein estimator
Kullback Leibler Distance
Markov Chain
Markov chain Monte Carlo
MCMC
MCMC Algorithm
MCMC Estimate
MCMC methods using BUGS software
MCMC Output
MCMC Sampling
Metropolis Hastings Algorithm
model assessment techniques
Posterior Distribution
Posterior Predictive Distribution
Prior Distribution
social science data analysis
Target Distribution
Transition Kernel
Uninformative Prior

Product details

  • ISBN 9781439862483
  • Weight: 1436g
  • Dimensions: 178 x 254mm
  • Publication Date: 11 Dec 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists

Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of the procedures and less on justifying procedures. The expanded examples reflect this updated approach.

New to the Third Edition

  • A chapter on Bayesian decision theory, covering Bayesian and frequentist decision theory as well as the connection of empirical Bayes with James–Stein estimation
  • A chapter on the practical implementation of MCMC methods using the BUGS software
  • Greatly expanded chapter on hierarchical models that shows how this area is well suited to the Bayesian paradigm
  • Many new applications from a variety of social science disciplines
  • Double the number of exercises, with 20 now in each chapter
  • Updated BaM package in R, including new datasets, code, and procedures for calling BUGS packages from R

This bestselling, highly praised text continues to be suitable for a range of courses, including an introductory course or a computing-centered course. It shows students in the social and behavioral sciences how to use Bayesian methods in practice, preparing them for sophisticated, real-world work in the field.

Jeff Gill is a professor in the Department of Political Science, the Division of Biostatistics, and the Department of Surgery (Public Health Sciences) at Washington University. He is the author of several books and has published numerous research articles. His research applies Bayesian modeling and data analysis to questions in general social science quantitative methodology, political behavior and institutions, and medical/health data analysis using computationally intensive tools. He received his B.A. from UCLA, MBA from Georgetown University, Ph.D. from American University, and Post-Doctorate from Harvard University.

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