Numerical Methods for Bayesian Inference

Leonhard Held and Daniel Sabanés Bové
Additional contact information
Leonhard Held: University of Zurich, Institute of Social and Preventive Medicine
Daniel Sabanés Bové: University of Zurich, Institute of Social and Preventive Medicine

Chapter 8 in Applied Statistical Inference, 2014, pp 247-289 from Springer

Abstract: Abstract This chapter describes numerical methods for Bayesian inference in non-conjugate settings. Standard numerical techniques and the Laplace approximation provide ways to numerically compute posterior characteristics of interest. Monte Carlo methods, including Monte Carlo integration, rejection and importance sampling as well as Markov chain Monte Carlo are described. Finally, numerical computation of the marginal likelihood, necessary for Bayesian model selection, is discussed. Exercises are given at the end.

Keywords: Posterior Distribution; Markov Chain Monte Carlo; Importance Sampling; Marginal Likelihood; Proposal Distribution (search for similar items in EconPapers)
Date: 2014
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:978-3-642-37887-4_8

Ordering information: This item can be ordered from
http://www.springer.com/9783642378874

DOI: 10.1007/978-3-642-37887-4_8

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 ().