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16,363
On the derivation of the Bayesian Information Criterion
, 2010
"... We present a careful derivation of the Bayesian Inference Criterion (BIC) for model selection. The BIC is viewed here as an approximation to the Bayes Factor. One of the main ingredients in the approximation, the use of Laplace’s method for approximating integrals, is explained well in the literatur ..."
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We present a careful derivation of the Bayesian Inference Criterion (BIC) for model selection. The BIC is viewed here as an approximation to the Bayes Factor. One of the main ingredients in the approximation, the use of Laplace’s method for approximating integrals, is explained well
Bayesian Information Criterion
"... validation of patient reported outcomes. Quality of Life Research. DOI: 10.1007/s11136-011- ..."
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validation of patient reported outcomes. Quality of Life Research. DOI: 10.1007/s11136-011-
AWidely Applicable Bayesian Information Criterion
"... A statistical model or a learning machine is called regular if the map taking a parameter to a prob-ability distribution is one-to-one and if its Fisher information matrix is always positive definite. If otherwise, it is called singular. In regular statistical models, the Bayes free energy, which is ..."
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the Bayes free energy using only training samples, because an RLCT depends on an unknown true distribution. In the present paper, we define a widely applicable Bayesian information criterion (WBIC) by the average log likelihood function over the posterior distribution with the inverse temperature 1 / logn
Speaker, Environment And Channel Change Detection And Clustering Via The Bayesian Information Criterion
, 1998
"... In this paper, we are interested in detecting changes in speaker identity, environmental condition and channel condition; we call this the problem of acoustic change detection. The input audio stream can be modeled as a Gaussian process in the cepstral space. We present a maximum likelihood approach ..."
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Cited by 272 (2 self)
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approach to detect turns of a Gaussian process; the decision of a turn is based on the Bayesian Information Criterion (BIC), a model selection criterion well-known in the statistics literature. The BIC criterion can also be applied as a termination criterion in hierarchical methods for clustering of audio
Bayesian information criterion for censored survival models
- Biometrics
"... 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 ..."
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Cited by 36 (2 self)
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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
Speaker Clustering Based on Bayesian Information Criterion *
"... This paper presents an effective method for clustering unknown speech utterances based on their associated speakers. The proposed method jointly optimizes the generated clusters and the number of clusters according to a Bayesian information criterion (BIC). The criterion assesses a partitioning of u ..."
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This paper presents an effective method for clustering unknown speech utterances based on their associated speakers. The proposed method jointly optimizes the generated clusters and the number of clusters according to a Bayesian information criterion (BIC). The criterion assesses a partitioning
Knee point detection on bayesian information criterion
- In ICTAI ’08: Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence
, 2008
"... The main challenge of cluster analysis is that the number of clusters or the number of model parameters is seldom known, and it must therefore be determined before clustering. Bayesian Information Criterion (BIC) often serves as a statistical criterion for model selection, which can also be used in ..."
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Cited by 1 (0 self)
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The main challenge of cluster analysis is that the number of clusters or the number of model parameters is seldom known, and it must therefore be determined before clustering. Bayesian Information Criterion (BIC) often serves as a statistical criterion for model selection, which can also be used
A BAYESIAN INFORMATION CRITERION FOR SINGULAR MODELS
"... Abstract. We consider approximate Bayesian model choice for model selec-tion problems that involve models whose Fisher-information matrices may fail to be invertible along other competing submodels. Such singular models do not obey the regularity conditions underlying the derivation of Schwarz’s Bay ..."
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Bayesian information criterion (BIC) and the penalty structure in BIC generally does not reflect the frequentist large-sample behavior of their marginal likelihood. While large-sample theory for the marginal likelihood of singular models has been developed recently, the resulting approximations depend
Results 1 - 10
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16,363
