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  3. Bayesian inference using Gibbs sampling - Wikipedia
Bayesian inference using Gibbs sampling - Wikipedia
From Wikipedia, the free encyclopedia

Bayesian inference using Gibbs sampling (BUGS) is a statistical software for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods. It was developed by David Spiegelhalter at the Medical Research Council Biostatistics Unit in Cambridge in 1989 and released as free software in 1991.[1][2]

The BUGS project has evolved through four main versions: ClassicBUGS,[3] WinBUGS,[4] OpenBUGS[1] and MultiBUGS.[5] MultiBUGS is built on the existing algorithms and tools in OpenBUGS and WinBUGS, which are no longer developed, and implements parallelization to speed up computation. Several R packages are available, R2MultiBUGS acts as an interface to MultiBUGS, while Nimble is an extension of the BUGS language.

Alternative implementations of the BUGS language include JAGS and Stan.

See also

References

  1. ^ a b Lunn, David; Spiegelhalter, David; Thomas, Andrew; Best, Nicky (2009). "The BUGS project: Evolution, critique and future directions". Statistics in Medicine. 28 (25): 3049–3067. doi:10.1002/sim.3680. PMID 19630097.
  2. ^ McGrayne, Sharon Bertsch (2012). The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries. Yale University Press. p. 226. ISBN 9780300188226.
  3. ^ Gilks, W. R.; Thomas, A.; Spiegelhalter, D. J. (1994). "A Language and Program for Complex Bayesian Modelling". The Statistician. 43 (1): 169–177. doi:10.2307/2348941. JSTOR 2348941.
  4. ^ Lunn, David J.; Thomas, Andrew; Best, Nicky; Spiegelhalter, David (2000). "WinBUGS—A Bayesian modelling framework: concepts, structure, and extensibility". Statistics and Computing. 10 (4): 325–337. doi:10.1023/A:1008929526011. S2CID 2722195.
  5. ^ Goudie, Robert J. B.; Turner, Rebecca M.; De Angelis, Daniela; Thomas, Andrew (2020). "MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference". Journal of Statistical Software. 95 (7): 1–20. doi:10.18637/jss.v095.i07. PMC 7116196. PMID 33071678.


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