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Introduction

  • Martin A. Tanner
Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

The goal of this book is to provide a unified introduction to a variety of computational algorithms that can be used as part of a Bayesian (posterior) analysis or as part of a likelihood analysis. These algorithms are tools and may be categorized using several taxonomies. The reader may find it useful to review these taxonomies as an aid to understanding how these tools complement, contrast and extend each other.

Keywords

Gibbs Sampler Posterior Density General Social Survey Metropolis Algorithm Data Augmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1996

Authors and Affiliations

  • Martin A. Tanner
    • 1
  1. 1.Department of StatisticsNorthwestern UniversityEvanstonUSA

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