📑 Table of Contents

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

edit

References

edit
  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.
edit


📚 Artikel Terkait di Wikipedia

Bayesian statistics

in Bayesian inference, Bayes' theorem can be used to estimate the parameters of a probability distribution or statistical model. Since Bayesian statistics

Bayesian inference

Bayesian inference (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is a method of statistical inference in which Bayes' theorem is used to calculate a probability

List of things named after Thomas Bayes

molecular phylogenetics Bayesian inference using Gibbs sampling – Statistical software for Bayesian inference Bayesian information criterion – Criterion

Statistical inference

regression-based inference. The use of any parametric model is viewed skeptically by most experts in sampling human populations: "most sampling statisticians

Bayesian structural time series

ready-to-use packages for calculating the BSTS model, which do not require strong mathematical background from a researcher. Bayesian inference using Gibbs sampling

Gibbs sampling

hence do not need to be sampled. Gibbs sampling is commonly used as a means of statistical inference, especially Bayesian inference. It is a randomized algorithm

Bugs

subscription digital streaming service Bugs (nickname) Bayesian inference using Gibbs sampling, a software package Birmingham University Guild of Students

Nicky Best

deviance information criterion in Bayesian inference[B][E] and as a developer of Bayesian inference using Gibbs sampling.[A][D] She is a former professor