November 2010
Beginner to intermediate
672 pages
19h 28m
English
Content preview from Doing Bayesian Data Analysis
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Chapter 7
Inferring a Binomial Proportion via the Metropolis Algorithm
Contents
7.1 A Simple Case of the Metropolis Algorithm
7.1.1 A Politician Stumbles on the Metropolis Algorithm
7.1.2 A Random Walk
7.1.3 General Properties of a Random Walk
7.1.4 Why We Care
7.1.5 Why It Works
7.2 The Metropolis Algorithm More Generally
7.2.1 “Burn-in,” Efficiency, and Convergence
7.2.2 Terminology: Markov Chain Monte Carlo
7.3 From the Sampled Posterior to the Three Goals
7.3.1 Estimation
7.3.2 Prediction
7.3.3 Model Comparison: Estimation of p(D) 137
7.4 MCMC in BUGS
7.4.1 Parameter Estimation with BUGS
7.4.2 BUGS for Prediction
7.4.3 BUGS for Model Comparison
7.5 Conclusion
7.6 R Code
7.6.1 R Code for a Home-Grown Metropolis Algorithm
7.7 Exercises
You furtive ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.