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Bayes Factors
, 1995
"... In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null ..."
Abstract

Cited by 1826 (74 self)
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In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null
Bayes factors and model uncertainty
 DEPARTMENT OF STATISTICS, UNIVERSITY OFWASHINGTON
, 1993
"... In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null ..."
Abstract

Cited by 124 (6 self)
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In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null
Bayes Factors: What they are and what they are not
 The American Statistician
, 1997
"... this paper, we show how Bayes factors suffer that same flaw. We investigate ..."
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Cited by 53 (0 self)
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this paper, we show how Bayes factors suffer that same flaw. We investigate
Bayes factor
"... The Poisson family constitutes a benchmark model for count data, which can ..."
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The Poisson family constitutes a benchmark model for count data, which can
On Bayes Factors for Nonparametric Alternatives
, 1996
"... this paper we derive global Bayes factors for the comparison of a parametric model with a nonparametric alternative. The alternative is constructed by embedding the parametric model in a mixture of Dirichlet Processes. Results include a general explicit form for partially exchangeable sequences as w ..."
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Cited by 12 (1 self)
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this paper we derive global Bayes factors for the comparison of a parametric model with a nonparametric alternative. The alternative is constructed by embedding the parametric model in a mixture of Dirichlet Processes. Results include a general explicit form for partially exchangeable sequences
PARTIAL INTRINSIC BAYES FACTOR
"... We have developed a new model selection criteria, the partial intrinsic Bayes factor, which is designed for cases when we select a model among a small number of candidate models. For example, we can choose only a few candidate models after exploring scatter plots. Based on this motivation, the parti ..."
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We have developed a new model selection criteria, the partial intrinsic Bayes factor, which is designed for cases when we select a model among a small number of candidate models. For example, we can choose only a few candidate models after exploring scatter plots. Based on this motivation
2. Understanding and estimating Bayes factors............................................................................................................................................................... 332
"... 2.1. Understanding Bayes factors............................................................................................................................................................................. 332 ..."
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2.1. Understanding Bayes factors............................................................................................................................................................................. 332
Bayes Factors for Goodness of Fit Testing
, 2003
"... We propose the use of the generalized fractional Bayes factor for testing fit in multinomial models. This is a nonasymptotic method that can be used to quantify the evidence for or against a submodel. We give expressions for the generalized fractional Bayes factor and we study its properties. In p ..."
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We propose the use of the generalized fractional Bayes factor for testing fit in multinomial models. This is a nonasymptotic method that can be used to quantify the evidence for or against a submodel. We give expressions for the generalized fractional Bayes factor and we study its properties
Bayes factor consistency in regression problems
, 2012
"... We investigate the asymptotic behavior of the Bayes factor for regression problems in which observations are not required to be independent and identically distributed and provide general results about consistency of the Bayes factor. Then we specialize our results to the model selection problem in ..."
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We investigate the asymptotic behavior of the Bayes factor for regression problems in which observations are not required to be independent and identically distributed and provide general results about consistency of the Bayes factor. Then we specialize our results to the model selection problem
Default Bayes factors for ANOVA designs.
 Journal of Mathematical Psychology,
, 2012
"... Abstract Bayes factors have been advocated as superior to pvalues for assessing statistical evidence in data. Despite the advantages of Bayes factors and the drawbacks of pvalues, inference by pvalues is still nearly ubiquitous. One impediment to adoption of Bayes factors is a lack of practical ..."
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Cited by 25 (4 self)
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Abstract Bayes factors have been advocated as superior to pvalues for assessing statistical evidence in data. Despite the advantages of Bayes factors and the drawbacks of pvalues, inference by pvalues is still nearly ubiquitous. One impediment to adoption of Bayes factors is a lack of practical
Results 1  10
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