Journal ArticleDOI
Bayes Factors for Linear and Log-Linear Models with Vague Prior Information
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This article is published in Journal of the royal statistical society series b-methodological.The article was published on 1982-07-01. It has received 289 citations till now. The article focuses on the topics: Bayes factor & Bayes estimator.read more
Citations
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Local computations with probabilities on graphical structures and their application to expert systems
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Variable selection via Gibbs sampling
TL;DR: In this paper, the Gibbs sampler is used to indirectly sample from the multinomial posterior distribution on the set of possible subset choices to identify the promising subsets by their more frequent appearance in the Gibbs sample.
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A weakly informative default prior distribution for logistic and other regression models
TL;DR: In this paper, the authors propose a new prior distribution for logistic regression models, called Cauchy prior, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients.
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Approximate Bayesian-inference With the Weighted Likelihood Bootstrap
TL;DR: The weighted likelihood bootstrap (WLB) as mentioned in this paper is a generalization of the Rubin's Bayesian bootstrap, which is used to simulate the posterior distribution of a posterior distribution.
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Assessment and Propagation of Model Uncertainty
TL;DR: In this article, a Bayesian approach to estimating structural uncertainty about unknown quantities is presented, which can be applied to forecasting the price of oil and the chance of catastrophic failure of the US space shuttle.
References
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Posterior odds ratios for selected regression hypotheses
Arnold Zellner,Aloysius Siow +1 more
TL;DR: Bayesian posterior odds ratios for frequently encountered hypotheses about parameters of the normal linear multiple regression model are derived and discussed in this paper, where it is shown that the posterior odds ratio can be well approximated by functions that are monotonic in usual sampling theory.
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Discrimination Among Mechanistic Models
George E. P. Box,William J. Hill +1 more
TL;DR: To discriminate among these a sequential procedure is developed in which calculations made after each experiment determine the most discriminatory process conditions for use in the next experiment.
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The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters
TL;DR: Raiffa and Schlaifer's theory of conjugate prior distributions is applied to Jeffrey's theory for simple normal sampling, for model I analysis of variance, and for univariate and multivariate Behrens-Fisher probelms as discussed by the authors.
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Bayes Factors and Choice Criteria for Linear Models
TL;DR: In this article, global and local Bayes factors are defined and their respective roles examined as choice criteria among alternative linear models, and the relationship of the global Bayes factor to the Lindley Paradox is examined.