The Practical Implementation of Bayesian Model Selection

By Hugh A. Chipman, Edward I. George, Robert E. McCulloch. 2001

491 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a5b40921ae03cc6fa34f532173a23c321c9ad3f7

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Citations in the corpus

Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies (2012, methodology): “This is equivalent to integrating out the intercept with respect to an improper, uniform prior (Chipman, George, and McCulloch 2001).”
Forecasting with Many Predictors (2006, methodology): “Bayesian model selection Bayesian model selection entails selecting the model with the highest posterior probability and using that model as the basis for forecasting; see the reviews by George (1999) and Chipman, George and McCulloch (2001).”
Predicting the present with Bayesian structural time series (2014, background): “This is known as Zellner’s g − prior (Zellner, 1986; Chipman et al. , 2001; Liang et al. , 2008).”
Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity (2006, background): “The integrated likelihood, also called the marginal likelihood or the normalizing constant, is an important quantity in Bayesian model comparison and testing: it is the key component of the Bayes factor (Kass and Raftery 1995; Chipman, George, and McCulloch 2001).”
A simple new approach to variable selection in regression, with application to genetic fine mapping (2018, methodology): “We assume that y and the columns of X have been centred to have mean 0, which avoids the need for an intercept term (Chipman et al., 2001).”
Skew mixture models for loss distributions: a Bayesian approach (2012, methodology): “The strategy is to calculate the Bayes factors (Kass et al., 1995 and Chipman et al., 2001), which correspond to the ratio between the marginal likelihood associated to each model i.e. m (y|ML) = ∫ L (θL|y) p (θL) dθL (2.14) where L (θL|y) is the likelihood defined in equation (2.1) while p (θL) is…”
Data Analysis Using Regression and Multilevel/Hierarchical Models: Understanding and summarizing the fitted models (2006, unknown): “Examples of hierarchical generalized linear modeling in the literature include Novick, Lewis, and Jackson (1973), Clayton and Kaldor (1987), Karim and Zeger (1992), Barry et al.”
Empirical Bayes vs. Fully Bayes Variable Selection (2008, background): “More recently, a wide variety of semiautomatic and objective Bayesian approaches to the variable selection problem have appeared in the literature, for example, see Berger and Pericchi (2001), Chipman et al. (2001), Clyde and George (2004), Casella and Moreno (2006) and the references therein.”
Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging (2009, unknown): “The bulk of the BMA literature (Raftery, 1995; Chipman et al., 2001), favors the natural-conjugate approach, which puts a conditionally normal prior on coefficients (βs|σ2,Ms) ∼ N(βs, σ 2V s), where βs and V s represent hyperparameters.”
An exploration of aspects of Bayesian multiple testing (2006, methodology): “3 Computational Implementation via Importance Sampling From (8), it is apparent that posterior computations could be approached via mixture model analysis; see Robert (1996) and Celeux, Hurn, and Robert (2000) for discussion and other references to computation in such situations.”
Objective Testing Procedures in Linear Models: Calibration of the p‐values (2006, background): “Some recent key papers are George & McCulloch (1993, 1997), George & Foster (2000), Chipman et al. (2001) and Casella & Moreno (2006).”