Bayesian Modeling Using WinBUGS

By Christian P. Robert, Ioannis Ntzoufras. 2009

1,135 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0393fc2f2c2a63e03532a3dd1cad0cfa09684583

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Citations in the corpus

Residual life estimation based on bivariate Wiener degradation process with time-scale transformations (2014, background): “For more information about WinBUGS, readers can refer to [37].”
Measure Transformer Semantics for Bayesian Machine Learning (2011, methodology): “other probabilistic form of Scheme, equipped with conditional sampling and a mechanism of stochastic memoization. In MIT-Church, queries are implemented usingMarkov chain Monte Carlo methods. WinBUGS [39] is a popular implementation of the BUGS language [14] for explicitly describing distributions s”
Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, neutral): “…novice reader to, among many others: Bolstad (2007); Carlin and Louis (2009); Christensen, Johnson, Branscum, and Hanson (2010); Depaoli and Boyajian, (2014); Gelman and Hill (2007); Kaplan, 2014; Kruschke (2010); Jackman (2009); Lynch (2007); Ntzoufras (2009); or van de Schoot and Depaoli (2014).”
Bayesian estimation supersedes the t test. (2013, methodology): “If the analyst desires a likelihood function other than one already built into JAGS, then the “Poisson zeros trick” can be used to specify virtually any likelihood function (e.g., Ntzoufras, 2009, p. 276).”
Guidelines for a graph-theoretic implementation of structural equation modeling (2012, methodology): “The response forms for human disturbance variables were as follows: buffer intrusion was modeled as a proportional odds (Ntzoufras 2009, Agresti 2010), as was hydrologic alteration, and soil disturbance was modeled as a binary Bernoulli response.”
Cognitive model decomposition of the BART: Assessment and application (2011, methodology): “We implemented the BART models in the WinBUGS environment (Lunn, Spiegelhalter, Thomas, & Best, 2009; Lunn, Thomas, Best, & Spiegelhalter, 2000; Ntzoufras, 2009), of which introductions for psychologists are given by Lee and Wagenmakers (2009) and Sheu and O’Curry (1998).”
Bayesian analyses : where to start and what to report (2014, methodology): “Likewise, AMOS (Arbuckle, 2006), BUGS (Ntzoufras, 2009), and Mplus (Muthén, 2010) can be used for estimating Bayesian latent variable models, which can also include multilevel or mixture extensions.”