Bayesian Inference for Hard Problems Using the Gibbs Sampler

Alan E. Gelfand and Bradley P. Carlin
Additional contact information
Alan E. Gelfand: University of Connecticut, Department of Statistics
Bradley P. Carlin: Carnegie Mellon University, Department of Statistics

A chapter in Computing Science and Statistics, 1992, pp 29-37 from Springer

Abstract: Abstract The Gibbs sampler has been proposed as a general method for Bayesian calculation in Gelfand and Smith (1990). Here we describe two challenging implementations of the sampler. One is in the context of order restricted parameters and is illustrated using the normal linear model. The second involves nonconjugacy and is illustrated using a generalized logistic regression model. Synthesis of the ideas contained in these two problems greatly expands the range of possibilities for successful utilization the Gibbs sampler

Keywords: Posterior Distribution; Bayesian Inference; Carbon Disulphide; Posterior Mode; Full Conditional Distribution (search for similar items in EconPapers)
Date: 1992
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2856-1_4

Ordering information: This item can be ordered from
http://www.springer.com/9781461228561

DOI: 10.1007/978-1-4612-2856-1_4

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().