The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Understanding the Metropolis-Hastings Algorithm
Abstract
We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions. A simple, intuitive derivation of this method is given along with guidance on implementation. Also discussed are two applications of the algorithm, one for implementing acceptance-rejection sampling when a blanketing function is not available and the other for implementing the algorithm with block-at-a-time scans. In the latter situation, many different algorithms, including the Gibbs sampler, are shown to be special cases of the Metropolis-Hastings algorithm. The methods are illustrated with examples.
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Cites
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Cited by
Inference of Population Structure Using Multilocus Genotype Data
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Discrete Choice Methods with Simulation
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Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
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Metadata
- Authors
- Chib, S., & Greenberg, E.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1995-11
- Volume
- 49
- Issue
- 4
- Pages
- 327-335
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2108207895
- Cited by
- 4,145
- References
- 6
- FWCI
- 28.20
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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