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Journal articles on the topic 'Bayesian Information Criterion'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Nguefack-Tsague, Georges, and Ingo Bulla. "A Focused Bayesian Information Criterion." Advances in Statistics 2014 (October 14, 2014): 1–8. http://dx.doi.org/10.1155/2014/504325.

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Myriads of model selection criteria (Bayesian and frequentist) have been proposed in the literature aiming at selecting a single model regardless of its intended use. An honorable exception in the frequentist perspective is the “focused information criterion” (FIC) aiming at selecting a model based on the parameter of interest (focus). This paper takes the same view in the Bayesian context; that is, a model may be good for one estimand but bad for another. The proposed method exploits the Bayesian model averaging (BMA) machinery to obtain a new criterion, the focused Bayesian model averaging (

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2

Bayarri, M. J., James O. Berger, Woncheol Jang, Surajit Ray, Luis R. Pericchi, and Ingmar Visser. "Prior-based Bayesian information criterion." Statistical Theory and Related Fields 3, no. 1 (2019): 2–13. http://dx.doi.org/10.1080/24754269.2019.1582126.

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3

Zhou, Shouhao. "Posterior Averaging Information Criterion." Entropy 25, no. 3 (2023): 468. http://dx.doi.org/10.3390/e25030468.

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We propose a new model selection method, named the posterior averaging information criterion, for Bayesian model assessment to minimize the risk of predicting independent future observations. The theoretical foundation is built on the Kullback–Leibler divergence to quantify the similarity between the proposed candidate model and the underlying true model. From a Bayesian perspective, our method evaluates the candidate models over the entire posterior distribution in terms of predicting a future independent observation. Without assuming that the true distribution is contained in the candidate m

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4

Li, Wentian, and Dale R. Nyholt. "Marker Selection by Akaike Information Criterion and Bayesian Information Criterion." Genetic Epidemiology 21, S1 (2001): S272—S277. http://dx.doi.org/10.1002/gepi.2001.21.s1.s272.

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5

Hirose, Kei, Shuichi Kawano, and Sadanori Konishi. "BAYESIAN FACTOR ANALYSIS AND INFORMATION CRITERION." Bulletin of informatics and cybernetics 40 (December 2008): 75–87. http://dx.doi.org/10.5109/18995.

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6

Wang, L. "Wilcoxon-type generalized Bayesian information criterion." Biometrika 96, no. 1 (2009): 163–73. http://dx.doi.org/10.1093/biomet/asn060.

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7

Calderón Rivera, Dayana Soret, Claudia Fernanda Navarrete López, and José Luis Díaz Arévalo. "Ajustes de distribuciones probabilísticas para la variable temperatura media multianual para el departamento de Boyacá (Colombia)." Ingeniería y Región 14, no. 2 (2016): 113. http://dx.doi.org/10.25054/22161325.698.

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En este trabajo se presenta un estudio acerca de la selección de la mejor distribución probabilística para la variable media multianual de la temperatura en el departamento de Boyacá (Colombia), como base para futuras estimaciones y proyecciones de la variable en condiciones de incertidumbre. Se seleccionaron las distribuciones Normal, Gamma, Weibull y LogNormal para ajustar los datos; y para encontrar cual distribución ajusta mejor los datos se utilizaron los criterios de información basados en la máxima verosimilitud de Akaike (Akaike Information Criterion) y Bayesiano (Bayesian Information

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8

Volinsky, Chris T., and Adrian E. Raftery. "Bayesian Information Criterion for Censored Survival Models." Biometrics 56, no. 1 (2000): 256–62. http://dx.doi.org/10.1111/j.0006-341x.2000.00256.x.

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9

Drton, Mathias, and Martyn Plummer. "A Bayesian information criterion for singular models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 2 (2017): 323–80. http://dx.doi.org/10.1111/rssb.12187.

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10

Lan, Wei, Hansheng Wang, and Chih-Ling Tsai. "A Bayesian information criterion for portfolio selection." Computational Statistics & Data Analysis 56, no. 1 (2012): 88–99. http://dx.doi.org/10.1016/j.csda.2011.06.012.

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11

Ando, Tomohiro. "Bayesian Model Averaging and Bayesian Predictive Information Criterion for Model Selection." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 38, no. 2 (2008): 243–57. http://dx.doi.org/10.14490/jjss.38.243.

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12

Zhao, Yuan, Xiaochuan Ke, Lei Huang, and Yuhang Xiao. "On Cycle-Period Estimation: A Bayesian Information Criterion." IEEE Transactions on Vehicular Technology 70, no. 4 (2021): 3949–54. http://dx.doi.org/10.1109/tvt.2021.3065380.

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13

Albahli, Saleh, and Ghulam Nabi Ahmad Hassan Yar. "Defect Prediction Using Akaike and Bayesian Information Criterion." Computer Systems Science and Engineering 41, no. 3 (2022): 1117–27. http://dx.doi.org/10.32604/csse.2022.021750.

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14

Zhu, Hongtu, Joseph G. Ibrahim, and Qingxia Chen. "Bayesian case-deletion model complexity and information criterion." Statistics and Its Interface 7, no. 4 (2014): 531–42. http://dx.doi.org/10.4310/sii.2014.v7.n4.a9.

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15

Chen, Jiahua, and Zeny Feng. "A discussion of ‘prior-based Bayesian information criterion’." Statistical Theory and Related Fields 3, no. 1 (2019): 14–16. http://dx.doi.org/10.1080/24754269.2019.1583628.

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16

Liu, Sifan, and Dongchu Sun. "Discussion of ‘Prior-based Bayesian Information Criterion (PBIC)’." Statistical Theory and Related Fields 3, no. 1 (2019): 24–25. http://dx.doi.org/10.1080/24754269.2019.1611142.

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17

Clarke, Bertrand S. "Discussion of ‘Prior-based Bayesian Information Criterion (PBIC)’." Statistical Theory and Related Fields 3, no. 1 (2019): 26–29. http://dx.doi.org/10.1080/24754269.2019.1611143.

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18

Hannig, Jan. "Discussion of ‘Prior-based Bayesian information criterion (PBIC)’." Statistical Theory and Related Fields 3, no. 1 (2019): 30–31. http://dx.doi.org/10.1080/24754269.2019.1611144.

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19

Mehrjou, Arash, Reshad Hosseini, and Babak Nadjar Araabi. "Improved Bayesian information criterion for mixture model selection." Pattern Recognition Letters 69 (January 2016): 22–27. http://dx.doi.org/10.1016/j.patrec.2015.10.004.

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20

Friel, N., J. P. McKeone, C. J. Oates, and A. N. Pettitt. "Investigation of the widely applicable Bayesian information criterion." Statistics and Computing 27, no. 3 (2016): 833–44. http://dx.doi.org/10.1007/s11222-016-9657-y.

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21

Hu, Jianwei, Hong Qin, Ting Yan, and Yunpeng Zhao. "Corrected Bayesian Information Criterion for Stochastic Block Models." Journal of the American Statistical Association 115, no. 532 (2019): 1771–83. http://dx.doi.org/10.1080/01621459.2019.1637744.

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22

Jones, Richard H. "Bayesian information criterion for longitudinal and clustered data." Statistics in Medicine 30, no. 25 (2011): 3050–56. http://dx.doi.org/10.1002/sim.4323.

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23

Neath, Andrew A., and Joseph E. Cavanaugh. "The Bayesian information criterion: background, derivation, and applications." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 2 (2011): 199–203. http://dx.doi.org/10.1002/wics.199.

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24

Sclove, Stanley L. "Using Model Selection Criteria to Choose the Number of Principal Components." Journal of Statistical Theory and Applications 20, no. 3 (2021): 450–61. http://dx.doi.org/10.1007/s44199-021-00002-4.

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AbstractThe use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

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25

Peiro, Ignacio Garcia. "How to Model in Zoology: Basic Concepts and Explanations." International Journal of Zoology and Animal Biology 7, no. 1 (2024): 1–2. http://dx.doi.org/10.23880/izab-16000556.

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The information criteria used for biologists-zoologists when comparing statistical models are summarized. The three criteria summarizes the quality (Akaike Information Criterion, AIC), complexity (Bayesian Information Criterion, BIC) and finally the reliability, fiability or accuracy (Deviance Information Criterion, DIC) of a model.

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26

Zou, Tianji, Kai Liu, Wenbo Wu, Ke Wang, and Congmin Lv. "Uncertainly Analysis of Prior Distribution in Accelerated Degradation Testing Bayesian Evaluation Method Based on Deviance Information Criterion." Symmetry 16, no. 2 (2024): 160. http://dx.doi.org/10.3390/sym16020160.

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The accelerated degradation testing (ADT) Bayesian evaluation method comprehensively utilizes product degradation data under accelerated stress levels collected over a short period of time and multiple sources of prior information, such as historical information, similar product information, simulation information, etc., to conduct life and reliability evaluation. Through the prior distribution, prior information affects the ADT Bayesian evaluation results ultimately. However, different evaluators may obtain different prior distributions based on the same prior information due to varying exper

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27

Choe, John Y., Yen-Chi Chen, and Nick Terry. "Information criterion for approximation of unnormalized densities." PLOS ONE 20, no. 3 (2025): e0317430. https://doi.org/10.1371/journal.pone.0317430.

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This paper considers the problem of approximating an unknown density when it can be evaluated up to a normalizing constant at a finite number of points. This density approximation problem is ubiquitous in statistics, such as approximating a posterior density for Bayesian inference and estimating an optimal density for importance sampling. We consider a parametric approximation approach and cast it as a model selection problem to find the best model in pre-specified distribution families (e.g., select the best number of Gaussian mixture components and their parameters). This problem cannot be a

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28

Ke, Xiaochuan, Yuan Zhao, and Lei Huang. "On Accurate Source Enumeration: A New Bayesian Information Criterion." IEEE Transactions on Signal Processing 69 (2021): 1012–27. http://dx.doi.org/10.1109/tsp.2021.3052052.

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29

Shao, Jun, and Sheng Zhang. "A discussion of ‘prior-based Bayesian information criterion (PBIC)’." Statistical Theory and Related Fields 3, no. 1 (2019): 19–21. http://dx.doi.org/10.1080/24754269.2019.1583086.

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30

Jiang, Jiming, and Thuan Nguyen. "A discussion of prior-based Bayesian information criterion (PBIC)." Statistical Theory and Related Fields 3, no. 1 (2019): 17–18. http://dx.doi.org/10.1080/24754269.2019.1583631.

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31

Shriner, Daniel, and Nengjun Yi. "Deviance information criterion (DIC) in Bayesian multiple QTL mapping." Computational Statistics & Data Analysis 53, no. 5 (2009): 1850–60. http://dx.doi.org/10.1016/j.csda.2008.01.016.

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32

Shaik, Thanveer, Xiaohui Tao, Lin Li, et al. "Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion." Pattern Recognition Letters 177 (January 2024): 121–27. http://dx.doi.org/10.1016/j.patrec.2023.12.004.

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33

Yulistiani, Selma, and Suliadi Suliadi. "Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi." STATISTIKA: Journal of Theoretical Statistics and Its Applications 19, no. 1 (2019): 29–37. http://dx.doi.org/10.29313/jstat.v19i1.4740.

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Time series data may be affected by special events or circumstances such as promotions, natural disasters, etc. These events can lead to inconsistent observations in the series called outliers. Because outliers can make invalid conclusions, it is important to carry out procedures in detecting outlier effects. In outlier detection there is one type of outlier, namely additive outlier (AO). The process of detecting additive outliers in the ARIMA model can be said as a model selection problem, where the candidate model assumes additive outliers at a certain time. In the selection of models there

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34

Aniyan, Anitta Susan, and Dais George. "BAYESIAN MODELING OF FINANCIAL DATA USING ESSCHER TRANSFORMED LAPLACE DISTRIBUTION IN STAN." Advances and Applications in Statistics 92, no. 7 (2025): 1031–56. https://doi.org/10.17654/0972361725046.

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This study investigates the application of Bayesian methods for estimating parameter of the Esscher transformed Laplace distribution, renowned for its ability to capture the asymmetry and heavy tails commonly observed in financial data. This paper also focuses on the Bayesian estimation of stress strength parameter using both squared error and LINEX loss functions. A simulation study is conducted to compare the performance of the proposed Bayesian estimators for the unknown parameter and stress strength parameter with maximum likelihood estimator based on mean squared error. For the simulation

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35

Wang, Yanjun, and Qun Liu. "Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships." Fisheries Research 77, no. 2 (2006): 220–25. http://dx.doi.org/10.1016/j.fishres.2005.08.011.

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36

Veerkamp, Wim J. J., and Martijn P. F. Berger. "Some New Item Selection Criteria for Adaptive Testing." Journal of Educational and Behavioral Statistics 22, no. 2 (1997): 203–26. http://dx.doi.org/10.3102/10769986022002203.

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In this study some alternative item selection criteria for adaptive testing are proposed. These criteria take into account the uncertainty of the ability estimates. A general weighted information criterion of which the usual maximum information criterion and the proposed alternative criteria are special cases is suggested. A small simulation study was conducted to compare the different criteria. The results showed that the likelihood weighted information criterion is a good alternative to the maximum information criterion. Another good alternative is a maximum information criterion with the ma

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37

Ando, T. "Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models." Biometrika 94, no. 2 (2007): 443–58. http://dx.doi.org/10.1093/biomet/asm017.

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38

R.M., Harindranath, and Jayanth Jacob. "Bayesian structural equation modelling tutorial for novice management researchers." Management Research Review 41, no. 11 (2018): 1254–70. http://dx.doi.org/10.1108/mrr-11-2017-0377.

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Purpose This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner. Design/methodology/approach D

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39

Gao, Zhanzhongyu, Xun Xiao, Yi-Ping Fang, Jing Rao, and Huadong Mo. "A Selective Review on Information Criteria in Multiple Change Point Detection." Entropy 26, no. 1 (2024): 50. http://dx.doi.org/10.3390/e26010050.

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Change points indicate significant shifts in the statistical properties in data streams at some time points. Detecting change points efficiently and effectively are essential for us to understand the underlying data-generating mechanism in modern data streams with versatile parameter-varying patterns. However, it becomes a highly challenging problem to locate multiple change points in the noisy data. Although the Bayesian information criterion has been proven to be an effective way of selecting multiple change points in an asymptotical sense, its finite sample performance could be deficient. I

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40

Hussein, Amjad, and Safaa K. Kadhem. "Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland." Open Engineering 12, no. 1 (2022): 204–14. http://dx.doi.org/10.1515/eng-2022-0024.

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Abstract This study investigates the spatial heterogeneity in the maximum monthly rainfall amounts reported by stations in Ireland from January 2018 to December 2020. The heterogeneity is modeled by the Bayesian normal mixture model with different ranks. The selection of the best model or the degree of heterogeneity is implemented using four criteria which are the modified Akaike information criterion, the modified Bayesian information criterion, the deviance information criterion, and the widely applicable information criterion. The estimation and model selection process is implemented using

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41

Lin, Da, Xin Xu, and Fangling Pu. "Bayesian Information Criterion Based Feature Filtering for the Fusion of Multiple Features in High-Spatial-Resolution Satellite Scene Classification." Journal of Sensors 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/142612.

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This paper presents a novel classification method for high-spatial-resolution satellite scene classification introducing Bayesian information criterion (BIC)-based feature filtering process to further eliminate opaque and redundant information between multiple features. Firstly, two diverse and complementary feature descriptors are extracted to characterize the satellite scene. Then, sparse canonical correlation analysis (SCCA) with penalty function is employed to fuse the extracted feature descriptors and remove the ambiguities and redundancies between them simultaneously. After that, a two-p

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42

Wang, Hang, and David Miller. "Improved Parsimonious Topic Modeling Based on the Bayesian Information Criterion." Entropy 22, no. 3 (2020): 326. http://dx.doi.org/10.3390/e22030326.

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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 dictionary explained by a universal shared model. Further, in LDA all topics are in principle present in every document. In contrast, PTM gives sparse topic representation, determining the (small) subset of relevant topics for each document. A customized Bayesian information criterion (BIC) was derived, balancing model complexity and goodness of fit, wi

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43

Tamura, Y., T. Sato, M. Ooe, and M. Ishiguro. "A procedure for tidal analysis with a Bayesian information criterion." Geophysical Journal International 104, no. 3 (2007): 507–16. http://dx.doi.org/10.1111/j.1365-246x.1991.tb05697.x.

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44

Lu, Zhihua, and Abdelhak M. Zoubir. "Generalized Bayesian Information Criterion for Source Enumeration in Array Processing." IEEE Transactions on Signal Processing 61, no. 6 (2013): 1470–80. http://dx.doi.org/10.1109/tsp.2012.2232661.

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45

WEAKLIEM, DAVID L. "A Critique of the Bayesian Information Criterion for Model Selection." Sociological Methods & Research 27, no. 3 (1999): 359–97. http://dx.doi.org/10.1177/0049124199027003002.

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46

Hannart, Alexis, and Philippe Naveau. "An Improved Bayesian Information Criterion for Multiple Change-Point Models." Technometrics 54, no. 3 (2012): 256–68. http://dx.doi.org/10.1080/00401706.2012.694780.

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47

WADA, Yasuhiko, Akira TAKEBE, Shigeo MATSUMOTO, and Nobuhisa KASHIWAGI. "Model Selection for Sire Evaluation by Akaike's Bayesian Information Criterion." Nihon Chikusan Gakkaiho 64, no. 4 (1993): 371–78. http://dx.doi.org/10.2508/chikusan.64.371.

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48

Lee, Eun Ryung, Hohsuk Noh, and Byeong U. Park. "Model Selection via Bayesian Information Criterion for Quantile Regression Models." Journal of the American Statistical Association 109, no. 505 (2014): 216–29. http://dx.doi.org/10.1080/01621459.2013.836975.

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49

Kasali, Jimoh, and Adediwura Alaba Adeyemi. "Model-Data Fit using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and The Sample-Size-Adjusted BIC." Square : Journal of Mathematics and Mathematics Education 4, no. 1 (2022): 43–51. http://dx.doi.org/10.21580/square.2022.4.1.11297.

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The study determined if the 1PL, 2PL, 3PL and 4PL item response theory models best fit the data from the 2016 NECO Mathematics objective tests. Ex-post facto design was adopted for the study. The population for the study comprised 1,022,474 candidates who enrolled and sat for June/July SSCE 2016 NECO Mathematics Examination. The sample comprised 276,338 candidates who sat for the examination in three purposively Geo political zones in Nigeria (i.e., S/West, S/East and N/West). The research instruments used for the study were Optical Marks Record Sheets for the National Examination Council (NEC

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50

Badshah, Waqar, and Mehmet Bulut. "Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence." Economies 8, no. 2 (2020): 49. http://dx.doi.org/10.3390/economies8020049.

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Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwis

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