Advertisement

Bayesian Inference: Modeling and Computation

  • Robert W. Keener
Chapter
Part of the Springer Texts in Statistics book series (STS)

Abstract

This chapter explores several practical issues for a Bayesian approach to inference. The first section explores an approach used to specify prior distributions called hierarchical modeling, based on hyperparameters and conditioning. Section 15.2 discusses the robustness to the choice of prior distribution. Sections 15.4 and 15.5 deal with the Metropolis–Hastings algorithm and the Gibbs sampler, simulation methods that can be used to approximate posterior expectations numerically. As background, Section 15.3 provides a brief introduction to Markov chains. Finally, Section 15.6 illustrates how Gibbs sampling can be used in a Bayesian approach to image processing.

Keywords

Markov Chain Posterior Distribution Prior Distribution Bayesian Inference Conditional Distribution 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer New York 2009

Authors and Affiliations

  • Robert W. Keener
    • 1
  1. 1.Department of StatisticsUniversity of MichiganAnn ArborUSA

Personalised recommendations