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Committee Date Time Place Paper Title / Authors Abstract Paper #
KBSE 2024-03-15
15:15		 Okinawa Okinawa Prefectual General Welfare Center
(Primary: On-site, Secondary: Online)		 Bayesian Statistical Analysis of Commit History Data Using WBIC for OSS Evolution Analysis
Toru Sugiyama, Takako Nakatani (OUJ) KBSE2023-90		 This paper utilizes the latest advancements in computational Bayesian statistics to analyze the commit histories of Open... [more] KBSE2023-90
pp.138-142
PRMU, IBISML, IPSJ-CVIM [detail] 2023-03-03
17:10		 Hokkaido Future University Hakodate
(Primary: On-site, Secondary: Online)		 Forest Construction of Gaussian and Discrete Variables based on WBIC
Ashraful Islam, Joe Suzuki (Osaka Univ.) PRMU2022-126 IBISML2022-133		 Mutual information is a metric that determines the association between two random variables by measuring the amount of i... [more] PRMU2022-126 IBISML2022-133
pp.371-377
SP, IPSJ-SLP
(Joint)		 2014-07-25
13:20		 Iwate Hotel Hanamaki [Invited Talk] Evaluation Criteria of Statistical Learning when Gaussian Approximation can not be Applied to Likelihood Function
Sumio Watanabe (Tokyo Inst. of Tech.) SP2014-68		 Conventional statistical asymptotic theory was established based on the assumption that the likelihood function can be a... [more] SP2014-68
pp.31-36
NC, MBE
(Joint)		 2014-03-18
13:40		 Tokyo Tamagawa University Computational validation of the information criterion WBIC by the exchange Monte Carlo method
Satoru Tokuda, Kenji Nagata (Univ. of Tokyo), Sumio Watanabe (Tokyo Inst. of Tech.), Masato Okada (Univ. of Tokyo/RIKEN) NC2013-109		 In the models with hierarchy like artificial neural networks and mixture models, asymptotic normality, which AIC and BIC... [more] NC2013-109
pp.121-126
IBISML 2013-11-12
15:45		 Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Model Selection of Layered Neural Networks using WBIC based on Steepest Descent and MCMC Method
Yusuke Tamai, Sumio Watanabe (Tokyo Inst. of Tech.) IBISML2013-36		 Many learning machines such as neural networks, normal mixtures, and hidden Markov Models contain hierarchical layers, h... [more] IBISML2013-36
pp.1-6
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