DocumentCode
3559078
Title
A Bit of Information Theory, and the Data Augmentation Algorithm Converges
Author
Yu, Yaming
Author_Institution
Dept. of Stat., Univ. of California, Irvine, CA
Volume
54
Issue
11
fYear
2008
Firstpage
5186
Lastpage
5188
Abstract
The data augmentation (DA) algorithm is a simple and powerful tool in statistical computing. In this note basic information theory is used to prove a nontrivial convergence theorem for the DA algorithm.
Keywords
information theory; statistical analysis; data augmentation algorithm; information theory; nontrivial convergence theorem; statistical computing; Bayesian methods; Convergence; Density measurement; Entropy; Extraterrestrial measurements; Information geometry; Information theory; Monte Carlo methods; Probability; Sampling methods; Gibbs sampling; I-projection; Kullback–Leibler divergence; Markov chain Monte Carlo; Pinsker´s inequality; information geometry; relative entropy; reverse I-projection; total variation;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.2008.929918
Filename
4655476
Link To Document