A novel approach to the Bayesian Information Criterion (BIC) is introduced. The new criterion redefines the penalty terms of the BIC, such that each parameter is penalized with the effective sample size is trained with. Contrary to Local-BIC, the proposed criterion scores overall clustering hypotheses and therefore is not restricted to hierarchical clustering algorithms. Contrary to Global-BIC,...

Speaker diarization is necessary with ubiquitous and individualized recorders. We focus on the specific task of speaker diarization from two information streams, two microphones, assigned to two participants of interest. In real scenarios, speakers may be co-located, in noisy environments with interfering speakers. Multistream diarization can exploit additional information and diarization fusio...

The Bayesian information criterion (BIC) is a model selection criterion that has previously been applied to speaker segmentation of broadcast news by several researchers. The BIC approach treats speaker segmentation as a model selection problem. As the BIC requires the estimation of the sample covariance matrix, its performance tends to deteriorate as the speaker-turn duration decreases. It is ...

We propose an omnivariate decision tree architecture which contains univariate, multivariate linear or nonlinear nodes, matching the complexity of the node to the complexity of the data reaching that node. We compare the use of different model selection techniques including AIC, BIC, and CV to choose between the three types of nodes on standard datasets from the UCI repository and see that such...

Recently, various types of mixture models have been developed for data sets having a hierarchical or multilevel structure (see, e,g., [9, 12]). Most of these models include finite mixture distributions at multiple levels of a hierarchical structure. In these multilevel mixture models, selection of the number of mixture component is more complex than in standard mixture models because one has to...

In this paper, an approach of penalized Bayesian information criterion (pBIC) for decision tree state tying is described. The pBIC is applied to two important applications. First, it is used as a decision tree growing criterion in place of the conventional approach of using a heuristic constant threshold. It is found that original BIC penalty is too low and will not lead to compact decision tre...

We investigate the Bayesian Information Criterion (BIC) for variable selection in models for censored survival data. Kass and Wasserman (1995) showed that BIC provides a close approximation to the Bayes factor when a unit-information prior on the parameter space is used. We propose a revision of the penalty term in BIC so that it is de ned in terms of the number of uncensored events instead of ...

Consider the simple normal linear regression model for estimation/prediction at a new design point. When the slope parameter is not obviously nonzero, hypothesis testing and model selection methods can be used for identifying the right model. We compare performance of such methods both theoretically and empirically from different perspectives for more insight. The testing approach, in spite of ...

A simple theoretical framework for the description and interpretation of spatially correlated modelling residuals is presented, and the resulting tools are found to provide a useful aid to model selection in the context of weak gravitational lensing. The description is focused upon the specific problem of modelling the spatial variation of a telescope point spread function (PSF) across the inst...

We have recently proposed a new information-based approach to model selection, the Frequentist Information Criterion (FIC), that reconciles information-based and frequentist inference. The purpose of this current paper is to provide a simple example of the application of this criterion and a demonstration of the natural emergence of model complexities with both AIC-like (N) and BIC-like (logN )...