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Time to death and its predictors among under-five children with acute pneumonia: a Bayesian parametric survival analysis. [PDF]
Pneumonia (Nathan)Marine BT, Mengistie DT.
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Graph showing the best k value via Bayesian information criterion analysis.
Tenena Silue (21276472) +5 more
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Marker Selection by Akaike Information Criterion and Bayesian Information Criterion
Genetic Epidemiology, 2001We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affectedâaffected versus affectedâunaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters.
Li, W., Nyholt, D.R.
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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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IEEE Transactions on Neural Networks and Learning Systems, 2023
This article considers the regression problem with sparse Bayesian learning (SBL) when the number of weights P is larger than the data size N , i.e., P >> N . The situation induces overfitting and makes regression tasks, such as prediction and basis selection, challenging. We show a strategy to address this problem.
Kazuaki Murayama, Shuichi Kawano
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This article considers the regression problem with sparse Bayesian learning (SBL) when the number of weights P is larger than the data size N , i.e., P >> N . The situation induces overfitting and makes regression tasks, such as prediction and basis selection, challenging. We show a strategy to address this problem.
Kazuaki Murayama, Shuichi Kawano
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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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Spectral Library Clustering Using a Bayesian Information Criterion
2016 Sensor Signal Processing for Defence (SSPD), 2016Unsupervised classification of spectral libraries provides groups of spectra which may be of use in material detection or identification processes for hyperspectral imagery. It can also be used to discover associations between materials' spectra that can help analysts to interpret spectral data.
Jonathan Piper, John Duselis
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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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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 +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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