Jump to ContentJump to Main Navigation
Bayesian Statistics for Beginnersa step-by-step approach$
Users without a subscription are not able to see the full content.

Therese Donovan and Ruth M. Mickey

Print publication date: 2019

Print ISBN-13: 9780198841296

Published to Oxford Scholarship Online: July 2019

DOI: 10.1093/oso/9780198841296.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2020. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 02 July 2020

The White House Problem Revisited: MCMC with the Metropolis–Hastings Algorithm

The White House Problem Revisited: MCMC with the Metropolis–Hastings Algorithm

Chapter:
(p.224) Chapter 15 The White House Problem Revisited: MCMC with the Metropolis–Hastings Algorithm
Source:
Bayesian Statistics for Beginners
Author(s):

Therese M. Donovan

Ruth M. Mickey

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198841296.003.0015

The “White House Problem” of Chapter 10 is revisited in this chapter. Markov Chain Monte Carlo (MCMC) is used to build the posterior distribution of the unknown parameter p, the probability that a famous person could gain access to the White House without invitation. The chapter highlights the Metropolis–Hastings algorithm in MCMC analysis, describing the process step by step. The posterior distribution generated in Chapter 10 using the beta-binomial conjugate is compared with the MCMC posterior distribution to show how successful the MCMC method can be. By the end of this chapter, the reader will have a firm understanding of the following concepts: Monte Carlo, Markov chain, Metropolis–Hastings algorithm, Metropolis–Hastings random walk, and Metropolis–Hastings independence sampler.

Keywords:   Monte Carlo, Markov chain, Metropolis–Hastings algorithm, Metropolis–Hastings random walk, Metropolis–Hastings independence sampler, Keith Hastings

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .