Bayesian Adaptive Sampling for Variable Selection and Model Averaging

By Merlise A. Clyde, Joyee Ghosh, Michael L. Littman. 2011

253 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/635690ea160e4cfae96feabe571e259c5c591e4f

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Citations in the corpus

Mixtures of g-priors for Bayesian model averaging with economic applications (2010, unknown): “Recently, other alternative methods were proposed, such as Bayesian Adaptive Sampling by Clyde et al. (2011) and Evolutionary Stochastic Search by Bottolo and Richardson (2010), which is designed to work for situations where k is orders of magnitude larger than n.”
Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies (2012, methodology): “The challenge lies in designing a Markov chain that explores the model space efficiently, and a variety of ways to deal with this issue have been suggested; see Bottolo and Richardson (2010), Clyde et al. (2011), and Dellaportas et al. (2002) for overviews.”
Bayesian Variable Selection Under Collinearity (2015, methodology): “The posterior computation for the different versions of g-priors is done by enumerating all 215 models with the BAS algorithm (Clyde, Ghosh, and Littman 2011).”
Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach (2011, methodology): “While these methods are better at identifying high probability models, both Heaton and Scott (2010) and Clyde, Ghosh, and Littman (2011) note substantial disagreement in estimates such as posterior inclusion probabilities between MCMC methods and these alternative stochastic search methods in…”
The Median Probability Model and Correlated Variables (2018, methodology): “The MPM is now routinely used for distilling posterior evidence towards variable selection; Clyde et al. (2011); Feldkircher (2012); Garcia-Donato and Martinez-Beneito (2013); Ghosh (2015); Piironen and Vehtari (2017) and Drachal (2018) are some of the articles that have used and dis- cussed the…”
Mixtures of g-Priors in Generalized Linear Models (2015, methodology): “As p increases (e.g., larger than 35) and enumerating the entiremodel space becomes impractical, stochastic search algorithms (see Clyde, Ghosh, and Littman 2011; García-Donato andMartínezBeneito 2013 and the references therein) can be employed, while avoiding computationally expensive model search…”
GENERALIZED DOUBLE PARETO SHRINKAGE. (2011, methodology): “For the calculation of competing estimators we used lars (Hastie and Efron (2011)), SIS (Fan et al. (2010)), monomvn (Gramacy (2010)) and BAS (Clyde and Littman (2005), Clyde, Ghosh, and Littman (2010)) packages in R.”
Model Averaging and Its Use in Economics (2017, methodology): “Clyde et al. (2011) remark that while the standard algorithms MC3 and SSVS are easy to implement, they may mix poorly when covariates are highly correlated.”
Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective (2014, methodology): “[1997] and Clyde and George [2004]. In this framework, posterior inferences concerning a quantity of interest ∆ are obtained via Bayesian model averaging (or BMA), which entails integrating over the the model space”
Sequential Monte Carlo on large binary sampling spaces (2011, methodology): “There are other recent approaches like Bayesian Adaptive Sampling (Clyde et al. 2011) or Evolutionary Stochastic Search (Bottolo and Richardson 2010) which also aim at overcoming the difficulties of multi-modal binary distributions.”