BUGS - Bayesian inference Using Gibbs Sampling Version 0.50

By David J. Spiegelhalter, Andrew Thomas, N. G. Best, Walter R. Gilks. 1995

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

An Introduction to Probabilistic Programming (2018, background): “…recognized that computerized systemization of the denotation of models and evaluators for inference was essential and so developed specialized languages for model writing and query answering, amongst them BUGS (Spiegelhalter et al., 1995) and, more recently, STAN (Stan Development Team, 2014).”
Probabilistic programming in Python using PyMC3 (2016, methodology): “One of the earliest to enjoy widespread usage was the BUGS language (Spiegelhalter et al., 1995), which allows for the easy speciﬁcation of Bayesian models, and ﬁtting them via Markov chain Monte Carlo methods.”
Edward: A library for probabilistic modeling, inference, and criticism (2016, background): “More targeted environments such as BUGS (Spiegelhalter et al., 1995), which focuses on Bayesian analysis of statistical models, helped launch the emerging field of probabilistic programming.”
Understanding predictive information criteria for Bayesian models (2013, background): “At the present time, DIC appears to be the predictive measure of choice in Bayesian applications, in part because of its incorporation in the popular BUGS package (Spiegelhalter et al., 1994, 2003).”
Quantifying heterogeneity in a meta‐analysis (2002, methodology): “The expression (11) also allows us to compute a Bayesian estimate of Á (as a version of H ), for example by adding this calculation into BUGS [22] code for a simple random eeects meta-analysis with non-informative priors [27].”
Neighborhood Level Spatial Analysis of the Relationship Between Alcohol Outlet Density and Criminal Violence (2005, methodology): “This software is available for free download and was developed by the MRC Biostatistics Unit in Cambridge and the Imperial College School of Medicine in London (Spiegelhalter et al., 1995).”
How Many Countries for Multilevel Modeling? A Comparison of Frequentist and Bayesian Approaches (2013, methodology): “More sophisticated priors have been proposed (Natarajan and Kass 2000; Gelman 2006); however, I employ specifications which are commonly used by researchers (e.g. Spiegelhalter et al. 1997; Gelman and Hill 2007).”
Variational inference for count response semiparametric regression (2013, unknown): “MCMC analysis was performed in BUGS.”