The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Convergence Diagnostics for Markov Chain Monte Carlo
Open accessAbstract
Markov chain Monte Carlo (MCMC) is one of the most useful approaches to scientific computing because of its flexible construction, ease of use, and generality. Indeed, MCMC is indispensable for performing Bayesian analysis. Two critical questions that MCMC practitioners need to address are where to start and when to stop the simulation. Although a great amount of research has gone into establishing convergence criteria and stopping rules with sound theoretical foundation, in practice, MCMC users often decide convergence by applying empirical diagnostic tools. This review article discusses the most widely used MCMC convergence diagnostic tools. Some recently proposed stopping rules with firm theoretical footing are also presented. The convergence diagnostics and stopping rules are illustrated using three detailed examples.
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Metadata
- Authors
- Roy, V.
- Venue
- Annual Review of Statistics and Its Application
- Publisher
- Annual Reviews
- Published
- 2020-03-09
- Volume
- 7
- Issue
- 1
- Pages
- 387-412
- ISSN
- 2326-8298 (ISSN-L)
- 2326-831X
- OpenAlex
- W2976194262
- Cited by
- 298
- References
- 51
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- bronze
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