Bayesian Inference

Dirk P. Kroese and Joshua Chan
Additional contact information
Dirk P. Kroese: The University of Queensland, School of Mathematics and Physics

Chapter Chapter 8 in Statistical Modeling and Computation, 2014, pp 227-262 from Springer

Abstract: Abstract Bayesian statistics is a branch of statistics that is centered around Bayes’ formula (1.8), which is repeated in (8.1) below. To fully appreciate Bayesian inference, it is important to understand that the type of statistical reasoning here is somewhat different from that in classical statistics. In particular, model parameters are usually treated as random rather than fixed quantities.

Keywords: Posterior Distribution; Bayesian Network; Prior Distribution; Bayesian Model; Gibbs Sampler (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:978-1-4614-8775-3_8

Ordering information: This item can be ordered from
http://www.springer.com/9781461487753

DOI: 10.1007/978-1-4614-8775-3_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 ().