Expected‐posterior prior distributions for model selection

By J. M. Pérez, James O. Berger. 2002

182 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/85bbdf89bfa2c3c96472ef9cc0c78f4b0776a9e1

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Citations in the corpus

Diagnostics of prior-data agreement in applied Bayesian analysis (2008, background): “, xq) ∈ χ is a MTS if the posterior prior (Pérez and Berger 2002) π(θ|x(l)) is proper and no subset of x(l) leads to a proper posterior.”
Extending conventional priors for testing general hypotheses in linear models (2007, background): “Then, the CPD (20), can be expressed as”
Predictive Bayesian Model Selection (2011, background): “As pointed out by Plummer (2008) , the log-likelihood is sensitive to re-parameterization. Changing the parameterization of the model parameter (} might change the definition of the posterior expectation of the transformed parameter, and hence the value of the utility function. On the other hand , t”
Compatibility of prior specifications across linear models (2008, methodology): “Within the objective Bayesian framework, the expected posterior prior (EPP) methodology of Pérez and Berger (2002) is a method to construct prior distributions for model comparison; see also Neal (2001) for related concepts.”
Marginal likelihood estimation via power posteriors (2008, background): “Other approaches to this problem include the intrinsic Bayes factor (Berger and Pericchi, 1996) and expected posterior prior (Perez and Berger, 2002).”
Bayesian model selection of informative hypotheses for repeated measurements (2009, methodology): “To resolve this issue we propose a similar method as the empirical expected-posterior prior (EEPP) of Pérez and Berger (2002).”
Model Averaging and Its Use in Economics (2017, unknown): “In addition, the mechanism of imaginary data forms the basis of the expected-posterior prior (Pérez and Berger, 2002).”
On the use of non‐local prior densities in Bayesian hypothesis tests (2010, methodology): “O’Hagan, 2000; De Santis and Spezzaferri, 2001) and intrinsic Bayes factor (Berger and Pericchi, 1996a; Berger and Pericchi, 1998; Berger and Mortera, 1999; Perez and Berger, 2002 ) methodologies have been used to implicitly define default prior densities under alternative hypotheses.”
Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo (2009, methodology): “Methods based on the posterior predictive p-values and conditional p-values are also emerging as popular measures of model fit [Bayarri and Berger, 2000; Pérez and Berger, 2002;Aitkin et al., 2005].”
Criteria for Bayesian model choice with application to variable selection (2012, background): “…and Ibrahim (1995), Kass and Wasserman (1995), Berger and Pericchi (1996), Moreno, Bertolino and Racugno (1998), De Santis and Spezzaferri (1999), Pérez and Berger (2002), Bayarri and Garćıa-Donato (2008), Liang et al. (2008), Cui and George (2008), Maruyama and George (2008), Maruyama and…”