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Mobile Information Recommendation Using Multi-Criteria Decision Making with Bayesian Network [PDF]

International Journal of Information Technology & Decision Making, 2015
The advancement of network technology and the popularization of the Internet lead to increased interest in information recommendation. This paper proposes a group recommendation system that takes the preferences of group users in mobile environment and applies the system to recommendation of restaurants.
Han-Saem Park, Moon-Hee Park, Sung-Bae Cho   +2 more
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Akaike and Bayesian Information Criteria for Hidden Markov Models

IEEE Signal Processing Letters, 2019
We propose the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) for model selection in hidden Markov models (HMM) when the number of states is unknown. The exact solutions exploit the properties of HMM that allow tractable forms of both criteria to be obtained while transgressing the common assumption in AIC and BIC model
Noura Dridi, Melita Hadzagic
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Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria

2008
From data mining to computer vision, from visual surveillance to biometrics research, from biomedical imaging to bioinformatics, and from multimedia retrieval to information management, a large amount of data are naturally represented by multidimensional arrays, i.e., tensors.
TAO, Dacheng, SUN, Jimeng, SHEN, Jialie, Wu, Xindong, LI, Xuelong, Maybank, Stephen J., Faloutsos, Christos   +6 more
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Information criteria for the predictive evaluation of bayesian models

Communications in Statistics - Theory and Methods, 1997
As a natural successor of the information criteria AIC and ABIC, information criteria for the Bayes models were developed by evaluating the bias of the log likelihood of the predictive distribution as an estimate of its expected log-likelihood. Considering two specific situations for the true distribution, two information criteria, PIC1 and PIC2 are ...
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Dependence of Bayesian Model Selection Criteria and Fisher Information Matrix on Sample Size

Mathematical Geosciences, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lu, Dan, Ye, Ming, Neuman, Shlomo P.
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Deformations Detection by a Bayesian Approach: Prior Information Representation and Testing Criteria Definition

2007
It is well known that the classical testing procedures are not able to detect any significant deformation when the estimated displacements stemming from repeated surveys are small with respect to their precisions. This is true even if the displacements show some internal consistency (e.g.
ALBERTELLA, ALBERTA, CAZZANIGA, NOEMI EMANUELA, SANSO', FERNANDO, F. Sacerdote, M. Crespi, L. Luzietti   +5 more
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Bayesian Network Retrieval Discrimination Criteria Model Based on Unbalanced Information

2018
Unbalanced sample data are usually ignored in the process of case matching, but these data also lead to misclassification during case matching. To solve this problem, a discrimination criteria model based on the Bayesian network and corresponding algorithm is proposed in our paper.
Man Xu, Dan Gan, Jiang Shen, Bang An
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Laplace approximations and Bayesian information criteria in possibly misspecified models

Communications in Statistics - Theory and Methods, 2017
ABSTRACTWe provide general conditions to ensure the valid Laplace approximations to the marginal likelihoods under model misspecification, and derive the Bayesian information criteria including all terms of order Op(1). Under conditions in theorem 1 of Lv and Liu [J. R. Statist. Soc.
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Subsampled Information Criteria for Bayesian Model Selection in the Big Data Setting

2019 IEEE International Conference on Big Data (Big Data), 2019
Bayesian methods face unprecedented challenges in the era of big data, as the evaluation of likelihood in each iteration is computationally intensive. To deal with this bottleneck, recent literature focus mostly on speeding up Markov chain Monte Carlo (MCMC). Model selection, which is an important topic, has not received much attention. In the Bayesian
Lijiang Geng, Yishu Xue, Guanyu Hu
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