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On Cycle-Period Estimation: A Bayesian Information Criterion
IEEE Transactions on Vehicular Technology, 2021Detection of cyclostationary (CS) signals has been addressed by means of generalized likelihood ratio test criteria. However, an accurate maximum likelihood estimator requires the estimation of cycle period (CP) as $a \ priori$ information, which has not yet been correctly addressed in the literature.
Yuan Zhao +3 more
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A Bayesian Information Criterion for Portfolio Selection
SSRN Electronic Journal, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei Lan +2 more
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On Accurate Source Enumeration: A New Bayesian Information Criterion
IEEE Transactions on Signal Processing, 2021This work addresses the issue of source number detection in the general asymptotic regime where the numbers of antennas and samples both tend to infinity but their ratio converges to a constant. Among the information criteria for source enumeration, Bayesian information criterion (BIC) is able to provide an elegant link between detection probability ...
Xiaochuan Ke, Yuan Zhao, Lei Huang
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Bayesian information criterion for multidimensional sinusoidal order selection
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017Detecting the sinusoidal order is a prerequisite step for parametric multidimensional sinusoidal frequency estimation methods, whose applications range from radar and wireless communications to nuclear magnetic resonance spectroscopy. Although the Bayesian information criterion (BIC) has been commonly applied for model order selection, its application ...
Jie Xiong 0003 +3 more
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Improved Bayesian information criterion for mixture model selection
Pattern Recognition Letters, 2016A new criterion for mixture model selection is proposed.Mathematical derivation of the criterion is justified.The proposed criterion works as good as the state-of-the-art criteria for large sample size.The proposed criterion outperforms the state-of-the-art criteria for small sample size.The proposed criterion performs well for real datasets.
Arash Mehrjou +2 more
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Bayesian information criterion for longitudinal and clustered data
Statistics in Medicine, 2011When 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.
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The Bayesian information criterion: background, derivation, and applications
WIREs Computational Statistics, 2011AbstractThe Bayesian information criterion (BIC) is one of the most widely known and pervasively used tools in statistical model selection. Its popularity is derived from its computational simplicity and effective performance in many modeling frameworks, including Bayesian applications where prior distributions may be elusive. The criterion was derived
Andrew A. Neath, Joseph E. Cavanaugh
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Bayesian fisher information criterion for sampling optimization in ASL-MRI
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010Pulsed Arterial Spin Labeling (PASL) techniques potentially allow the absolute, non-invasive quantification of brain perfusion using Magnetic Resonance Imaging (MRI). This can be achieved by fitting a kinetic model to the data acquired at a number of inversion times (TI).
João M. Sanches +2 more
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Audio Segmentation via Tri-Model Bayesian Information Criterion
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007This paper addresses the problem of audio segmentation in practical media (e.g. TV series, movies and etc.) which usually consists of segments in various lengths with quite a portion of short ones. An unsupervised audio segmentation approach is presented, including a segmentation-stage to detect potential acoustic changes, and a refinement-stage to ...
Yunfeng Du +4 more
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A Critique of the Bayesian Information Criterion for Model Selection
Sociological Methods & Research, 1999The Bayesian information criterion (BIC) has become a popular criterion for model selection in recent years. The BIC is intended to provide a measure of the weight of evidence favoring one model over another, or Bayes factor. It has, however, some important drawbacks that are not widely recognized. First, Bayes factors depend on prior beliefs about the
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