Prior-based Bayesian information criterion [PDF]
Statistical Theory and Related Fields, 2019 We present a new approach to model selection and Bayes factor determination, based on Laplace expansions (as in BIC), which we call Prior-based Bayes Information Criterion (PBIC).
M. J. Bayarri +5 more
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Improved Parsimonious Topic Modeling Based on the Bayesian Information Criterion [PDF]
Entropy, 2020 In a previous work, a parsimonious topic model (PTM) was proposed for text corpora. In that work, unlike LDA, the modeling determined a subset of salient words for each topic, with topic-specific probabilities, with the rest of the words in the ...
Hang Wang, David Miller
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A discussion of ‘prior-based Bayesian information criterion’
Statistical Theory and Related Fields, 2019 We would like to thank the authors (Bayarri et al., 2018) for their interesting and provoking paper, and we wish to discuss some issues related to sample size in general and the number of covariate...
Jiahua Chen, Zeny Feng
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BAYESIAN FACTOR ANALYSIS AND INFORMATION CRITERION [PDF]
Bulletin of informatics and cybernetics, 2008 Summary: Factor analysis is one of the most popular methods of multivariate statistical analysis. This technique has been widely used in the social and behavioral sciences to explore the covariance structure among observed variables in terms of a few unobservable variables.
Kei Hirose +2 more
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Performance of Akaike Information Criterion and Bayesian Information Criterion in Selecting Partition Models and Mixture Models. [PDF]
Syst Biol, 2023 AbstractIn molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages.
Liu Q +3 more
europepmc +3 more sources
Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets [PDF]
BMC Evolutionary Biology, 2010 Background Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data.
Luo Arong +7 more
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Discussion of ‘Prior-based Bayesian Information Criterion (PBIC)’
Statistical Theory and Related Fields, 2019 We congratulate the authors for this engaging article. It gives us an opportunity to gaze at the idea of BIC and its generalisations.
Bertrand S. Clarke
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A Bayesian information criterion for singular models [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2016 We consider approximate Bayesian model choice for model selection problems that involve models whose Fisher-information matrices may fail to be invertible along other competing submodels.
Drton, Mathias, Plummer, Martyn
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A Widely Applicable Bayesian Information Criterion [PDF]
, 2012 A statistical model or a learning machine is called regular if the map taking a parameter to a probability distribution is one-to-one and if its Fisher information matrix is always positive definite. If otherwise, it is called singular.
Watanabe, Sumio
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Bayesian information criterion for longitudinal and clustered data [PDF]
Statistics in Medicine, 2011 When a number of models are fit to the same data set, one method of choosing the ‘best’ model is to select the model for which Akaike's information criterion (AIC) is lowest. AIC applies when maximum likelihood is used to estimate the unknown parameters in the model.
Richard H. Jones
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