Understanding the Hastings Algorithm

The Hastings algorithm is a key tool in computational science. While mathematically justified by detailed balance, it can be conceptually difficult to grasp. Here, we present two complementary and intuitive ways to derive and understand the algorithm. In our framework, it is straightforward to see t...

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Bibliographic Details
Published in:	Communications in statistics. Simulation and computation Vol. 44; no. 2; pp. 332 - 349
Main Authors:	Minh, David D. L., Minh, Do Le (Paul)
Format:	Journal Article
Language:	English
Published:	Philadelphia Taylor & Francis 01-01-2015 Taylor & Francis Ltd
Subjects:	Acceptance Algorithms Balancing Computation Computer simulation Hastings algorithm Markov analysis Markov chain Monte Carlo Metropolis-Hastings algorithm Monte Carlo simulation Primary 65C05 Secondary 78M31 Simulation
Online Access:	Get full text
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Summary:	The Hastings algorithm is a key tool in computational science. While mathematically justified by detailed balance, it can be conceptually difficult to grasp. Here, we present two complementary and intuitive ways to derive and understand the algorithm. In our framework, it is straightforward to see that the celebrated Metropolis-Hastings algorithm has the highest acceptance probability of all Hastings algorithms.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0361-0918 1532-4141
DOI:	10.1080/03610918.2013.777455