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401
EXTENDED BIC CRITERION FOR MODEL SELECTION
, 2002
"... Abstract. Model selection is commonly based on some variation of the BIC or minimum message length criteria, such as MML and MDL. In either case the criterion is split into two terms: one for the model (data code length/model complexity) and one for the data given the model (message length/data like ..."
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Abstract. Model selection is commonly based on some variation of the BIC or minimum message length criteria, such as MML and MDL. In either case the criterion is split into two terms: one for the model (data code length/model complexity) and one for the data given the model (message length
Version 1.02 Date 20100518 Title Bayesian multilocus QTL analysis based on the BIC criterion
, 2013
"... Description R package for a nonMCMC approximate multilocus Bayesian model selection approach to the analysis of quantitative trait loci (QTL). The method and models are described in (Ball, R. D. Genetics 159: 13511364, 2001; ..."
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Description R package for a nonMCMC approximate multilocus Bayesian model selection approach to the analysis of quantitative trait loci (QTL). The method and models are described in (Ball, R. D. Genetics 159: 13511364, 2001;
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 wellknown in the statistics literature. The BIC criterion can also be applied as a termination criterion in hierarchical methods for clustering of audio
Xmeans: Extending Kmeans with Efficient Estimation of the Number of Clusters
 In Proceedings of the 17th International Conf. on Machine Learning
, 2000
"... Despite its popularity for general clustering, Kmeans suffers three major shortcomings; it scales poorly computationally, the number of clusters K has to be supplied by the user, and the search is prone to local minima. We propose solutions for the first two problems, and a partial remedy for the t ..."
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Cited by 418 (5 self)
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for the third. Building on prior work for algorithmic acceleration that is not based on approximation, we introduce a new algorithm that efficiently, searches the space of cluster locations and number of clusters to optimize the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC
The consistency of the BIC Markov order estimator.
"... . The Bayesian Information Criterion (BIC) estimates the order of a Markov chain (with finite alphabet A) from observation of a sample path x 1 ; x 2 ; : : : ; x n , as that value k = k that minimizes the sum of the negative logarithm of the kth order maximum likelihood and the penalty term jAj ..."
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Cited by 67 (3 self)
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. The Bayesian Information Criterion (BIC) estimates the order of a Markov chain (with finite alphabet A) from observation of a sample path x 1 ; x 2 ; : : : ; x n , as that value k = k that minimizes the sum of the negative logarithm of the kth order maximum likelihood and the penalty term j
An Adaptive LASSOPenalized BIC
"... Mixture models are becoming a popular tool for the clustering and classification of highdimensional data. In such high dimensional applications, model selection is problematic. The Bayesian information criterion, which is popular in lower dimensional applications, tends to underestimate the true n ..."
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Mixture models are becoming a popular tool for the clustering and classification of highdimensional data. In such high dimensional applications, model selection is problematic. The Bayesian information criterion, which is popular in lower dimensional applications, tends to underestimate the true
Can the Strengths of AIC and BIC Be Shared?
 BIOMETRICA
, 2003
"... It is well known that AIC and BIC have different properties in model selection. BIC is consistent in the sense that if the true model is among the candidates, the probability of selecting the true model approaches 1. On the other hand, AIC is minimaxrate optimal for both parametric and nonparame ..."
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Cited by 21 (2 self)
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mentioned main strengths of AIC and BIC cannot be shared. That is, for any model selection criterion to be consistent, it must behave supoptimally compared to AIC in terms of mean average squared error.
Speaker change detection using BIC: A comparison on two datasets
 in Proc. 2006 IEEE Int. Symp. Communications, Control, and Signal Processing
, 2006
"... AbstractThis paper addresses the problem of unsupervised speaker change detection. We assume that there is no prior knowledge of the number of speakers or their identities. Two methods are tested. The first method uses the Bayesian Information Criterion (BIC), investigates the AudioSpectrumCentroi ..."
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Cited by 3 (3 self)
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AbstractThis paper addresses the problem of unsupervised speaker change detection. We assume that there is no prior knowledge of the number of speakers or their identities. Two methods are tested. The first method uses the Bayesian Information Criterion (BIC), investigates the Audio
Context tree estimation for not necessarily finite memory processes, via BIC and MDL
 IEEE Trans. Inf. Theory
, 2006
"... The concept of context tree, usually defined for finite memory processes, is extended to arbitrary stationary ergodic processes (with finite alphabet). These context trees are not necessarily complete, and may be of infinite depth. The familiar BIC and MDL principles are shown to provide strongly co ..."
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Cited by 55 (1 self)
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The concept of context tree, usually defined for finite memory processes, is extended to arbitrary stationary ergodic processes (with finite alphabet). These context trees are not necessarily complete, and may be of infinite depth. The familiar BIC and MDL principles are shown to provide strongly
SelfOrganizingMaps with BIC for Speaker Clustering
 IDIAP Research Report 0260, IDIAP Research Institute
, 2002
"... Abstract. A new approach is presented for clustering the speakers from unlabeled and unsegmented conversation, when the number of speakers is unknown. In this approach, each speaker is modeled by a SelfOrganizingMap (SOM). For estimation of the number of clusters the Bayesian Information Criterion ..."
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Cited by 8 (0 self)
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Criterion (BIC) is applied. This approach was tested on the NIST 1996 HUB4 evaluation test in terms of speaker and cluster purities. Results indicate that the combined SOMBIC approach can lead to better clustering results than the baseline system. IDIAPRR 0260 2 1.
Results 1  10
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401