Sampling from Any Distribution by MCMC
Marcel van Oijen ()
Chapter Chapter 6 in Bayesian Compendium, 2020, pp 33-38 from Springer
Abstract:
Abstract The Bayesian approach to parameter estimation requires modellers to make a major mental shift: we no longer aim to find a single ‘best’ parameter vector—instead we aim to determine the posterior probability distribution for the parameters.
Date: 2020
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:978-3-030-55897-0_6
Ordering information: This item can be ordered from
http://www.springer.com/9783030558970
DOI: 10.1007/978-3-030-55897-0_6
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 ().