Bayesian Model Choice: Asymptotics and Exact Calculations

By Alan E. Gelfand, Dipak K. Dey. 1994

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Citations in the corpus

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, methodology): “The product of the CPOs, called the pseudo-marginal likelihood (PsML), has been proposed as a proxy for the marginal likelihood f (y) (Gelfand and Dey, 1994; Gelfand, 1996).”
A Bayesian Log-Normal Model for Multivariate Loss Reserving (2012, background): “Further details on the calculation of CPO can be found in Gelfand and Dey (1994) and Gelfand (1996).”
A survey of Bayesian predictive methods for model assessment, selection and comparison (2012, methodology): “…methods for model assessment and comparison have been proposed by several authors: for early accounts see Stone (1974); Geisser (1975) and for the Bayesian cross-validation see Geisser and Eddy (1979); Gelfand, Dey and Chang (1992); Gelfand and Dey (1994); Bernardo and Smith (1994); Gelfand (1996).”
Trans-dimensional inverse problems, model comparison and the evidence (2006, methodology): “(For summaries see Smith 1991; Gelfand & Smith 1990; Smith & Roberts 1993).”
A simulation-based approach to Bayesian sample size determination for performance under a given model and for separating models (2002, background): “In the literature, in the case of regular models (models where dimension remains fixed as sample size increases), many screening criteria, Si(y(n)) can be expressed in the form Si(y(n)) = lnf (y(n) | θ̂ (n)i ,Mi) − k(n,pi)+ Op(1) (14) [Gelfand and Dey (1994)].”
FECUNDITY OF TREES AND THE COLONIZATION–COMPETITION HYPOTHESIS (2004, background): “Second, there is no consensus on model-selection methods for hierarchical models (Gelfand and Dey 1994).”
Computing Bayes Factors by Combining Simulation and Asymptotic Approximations (1997, background): “Gelfand and Dey (1994) and Tierney (personal communication) proposed the estimator”
Inference for Mixtures of Finite Polya Tree Models (2006, methodology): “Ishwaran and Zarepour (2000) and Green and Richardson (2001) showed that the DMA model approximates a DPM model as N gets large.”
Model selection and psychological theory: A discussion of the differences between the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) (2012, unknown): “Using the likelihood as a model selection criterion in itself is not advised, as it is well-known to select overly parameterized models (Gelfand & Dey, 1994).”