The analysis of R×C contingency tables usually features a test for independence between row and column counts. Throughout the social sciences, the adequacy of the independence hypothesis is generally evaluated by the outcome of a classical p-value null-hypothesis significance test. Unfortunately, however, the classical p-value comes with a number of well-documented drawbacks. Here we outline an...

Statistical inference in psychology has traditionally relied heavily on p-value significance testing. This approach to drawing conclusions from data, however, has been widely criticized, and two types of remedies have been advocated. The first proposal is to supplement p values with complementary measures of evidence, such as effect sizes. The second is to replace inference with Bayesian measur...

Current molecular methods of species delimitation are limited by the types of species delimitation models and scenarios that can be tested. Bayes factors allow for more flexibility in testing non-nested species delimitation models and hypotheses of individual assignment to alternative lineages. Here, we examined the efficacy of Bayes factors in delimiting species through simulations and empiric...

Bayes Factors play an important role in comparing the fit of models ranging from multiple regression to mixture models. Full Bayesian analysis calculates a Bayes Factor from an explicit prior distribution. However, computational limitations or lack of an appropriate prior sometimes prevent researchers from using an exact Bayes Factor. Instead, it is approximated, often using Schwarz’s (1978) Ba...

Hoijtink, Kooten, and Hulsker ( 2016 ) present a method for choosing the prior distribution for an analysis with Bayes factor that is based on controlling error rates, which they advocate as an alternative to our more subjective methods (Morey & Rouder, 2014 ; Rouder, Speckman, Sun, Morey, & Iverson, 2009 ; Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011 ). We show that the method they adv...

We review Bayesian approaches to model testing in general and to the assessment of topological hypotheses in particular. We show that the standard way of setting up Bayes factor tests of the monophyly of a group, or the placement of a sample sequence in a known reference tree, can be misleading. The reason for this is related to the well-known dependency of Bayes factors on model-specific prior...

Consider the problem of comparing parametric models M 1 ; : : : ; M k , when at least one of the models has an improper prior N i (i). Using the Bayes factor for comparing among these is not feasible due to arbitrary multiplicative constants in N (i). In this work we suggest adjusting the initial priors for each model, N i , by i (i) = Z N i (i jy)m (y)dy where m is a suitable predictive measur...

We have already proposed a constraint-based learning Bayesian network method using Bayes factor. Since a conditional independence test using Bayes factor has consistency, the learning method improves the learning accuracy of the traditional constraint-based learning methods. Additionally, the method is expected to learn larger network structures than the traditional methods do because it greatl...

An important part of a latent class analysis concerns the selection of the number of latent classes. In this paper, we discuss the Bayes factor as a selection tool. The discussion will focus on two aspects: (i) the computation of the Bayes factor and (ii) prior sensitivity. To deal with prior sensitivity, we propose to extend the model with a prior for the hyperparameters. We further discuss th...

This paper proposes the use of the Bayes Factor as a distance metric for speaker segmentation within a speaker diarization system. The proposed approach uses a pair of constant sized, sliding windows to compute the value of the Bayes Factor between the adjacent windows over the entire audio. Results obtained on the 2002 Rich Transcription Evaluation dataset show an improved segmentation perform...