Monte Carlo Methods in Bayesian Computation

By Michael Conklin. 2001

721 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

A Kernelized Stein Discrepancy for Goodness-of-fit Tests (2016, methodology): “, we have s q = r xlogf(x), independent of Z; calculating Zinvolves a high dimension integration, and has been the major challenge for likelihood-based and Bayesian methods for model evaluation (e.g.,Chen et al., 2012). With an i.i.d. sample fx igdrawn from the (unknown) p(x), the form (2) also enab”
Improving marginal likelihood estimation for Bayesian phylogenetic model selection. (2011, background): “Because (for K large) p β k 1 is only slightly more dispersed than p β k , it serves as an excellent importance distribution (Chen et al. 2000, p. 134).”
Bayesian Analysis of the Heterogeneity Model (2004, methodology): “As this model is the special case of (1) whereK we expect a similar influence of the parameterization for the more general model which conditional on SN could be regarded as the combination of K normal linear mixed models with the random effects of each group being independent from each other.”
Probabilistic evaluation of handwriting evidence: likelihood ratio for authorship (2008, methodology): “A comprehensive review of the methods was given by Chen et al. (2000).”
Bayesian inference in a sample selection model (2011, background): “Chen et al. (2000) provide an excellent overview and discussion of available methods.”
Approximate Bayesian inference for quantiles (2005, background): “One possibility is to approximate the likelihood function by a substitution likelihood that depends only on θ, an idea conceptually related to Bayesian empirical likelihood (Lazar, 2003) and to the approach of Chernozhukov and Hong (2003). Lavine (1995) proposed a form for the substitution likelihoo”
Which Side are You on? Identifying Perspectives at the Document and Sentence Levels (2006, methodology): “2 we derive the Gibbs samplers (Chen et al., 2000) for the Latent Sentence Perspective Model as follows,”
The Markov chain Monte Carlo revolution (2008, background): “The useful books [15] or [62] brings this up to date.”