1995
DOI: 10.1080/01621459.1995.10476592
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A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion

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Cited by 929 publications
(486 citation statements)
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“…To this end we used the method proposed by Wagenmakers (2007) and Masson (2011). This method uses Bayesian information criteria (BIC), calculated using a simple transformation of sum-of-squares values generated by the standard ANOVA, to estimate the Bayes factor and generate p ( H 0 | D ), assuming a “unit information prior” (for further details, see Kass & Wasserman, 1995; see also Jarosz & Wiley, 2014). …”
Section: Resultsmentioning
confidence: 99%
“…To this end we used the method proposed by Wagenmakers (2007) and Masson (2011). This method uses Bayesian information criteria (BIC), calculated using a simple transformation of sum-of-squares values generated by the standard ANOVA, to estimate the Bayes factor and generate p ( H 0 | D ), assuming a “unit information prior” (for further details, see Kass & Wasserman, 1995; see also Jarosz & Wiley, 2014). …”
Section: Resultsmentioning
confidence: 99%
“…Note that the information in the data about β can be conceptualized as (Kass & Wasserman, 1995). Hence, g is a scaling factor controlling the information that we give the prior on β , relative to the information in the sample.…”
Section: Default Prior Distributions For the Linear Modelmentioning
confidence: 99%
“…This approximation is particularly good if the models under comparison are nested, such that one is a simplified version of the other (Kass & Wasserman, 1995). Another advantage of the BIC is that it is relatively straightforward to compute; the BIC is given by BIC ϭ Ϫ2 ϫ ln L ϩ k ϫ ln n, where ln L is the log maximum likelihood, k is the number of free parameters, and n is the sample size.…”
Section: Alternative Methods For Comparing Toolbox Modelsmentioning
confidence: 99%
“…These priors form an integral part of the model, and they are informed by theoretical considerations and possibly also by available prior knowledge. Selecting appropriate prior distributions is of ongoing concern to Bayesian statisticians (e.g., Kass & Wasserman, 1995;Liang, Paulo, Molina, Clyde, & Berger, 2008). In some cases, for example, if the parameter space is bounded, the absence of prior knowledge can be expressed though uniform distributions, indicating that all values within the predefined range are equally likely a priori.…”
Section: Formal Specification Of a Cognitive Toolboxmentioning
confidence: 99%