This paper proposes a novel Bayesian approximation inference method for mixture modeling. Our key idea is to factorize marginal log-likelihood using a variational distribution over latent variables. An asymptotic approximation, a factorized information criterion (FIC), is obtained by applying the Laplace method to each of the factorized components. In order to evaluate FIC, we propose factorize...

This paper extends factorized asymptotic Bayesian (FAB) inference for latent feature models (LFMs). FAB inference has not been applicable to models, including LFMs, without a specific condition on the Hessian matrix of a complete loglikelihood, which is required to derive a “factorized information criterion” (FIC). Our asymptotic analysis of the Hessian matrix of LFMs shows that FIC of LFMs has...

Self localization seems necessary for mobile robot navigation. The conventional method such as geometric reconstruction from landmark observations is generally time-consuming and prone to errors. This paper proposes a method which constructs a decision tree and prediction trees of the landmark appearance that enable a mobile robot with a limited visual angle to observe efficiently and make deci...

Visual attention is one of the most important issues for a vision guided mobile robot. Methods have been proposed for visual attention control based on information criterion[3, 4]. However, the robot had to stop walking for observation and decision. This paper presents a method which enables observation and decision more efficiently and adaptively while it is walking. The method uses the expect...

Model selection based on classical information criteria, such as BIC, is generally computationally demanding, but its properties are well studied. On the other hand, model selection based on parameter shrinkage by l1-type penalties is computationally efficient. In this paper we make an attempt to combine their strengths, and propose a simple approach that penalizes the likelihood with data-depe...

Hierarchical random e ects models are steadily increasing in popularity as statistical tools in clinical trial research They can be used to estimate treatment or other covariate e ects in single study analyses coordinated over multiple clinical units and can also be extended to a wide variety of cross study applications After reviewing the single study case we use data from ve di erent protocol...

Logical Probability (LP) is strictly distinguished from Statistical Probability (SP). To measure semantic information or confirm hypotheses, we need to use sampling distribution (conditional SP function) to test or confirm fuzzy truth function (conditional LP function). The Semantic Information Measure (SIM) proposed is compatible with Shannon’s information theory and Fisher’s likelihood method...

1 Kitano Symbiotic Systems Project, ERATO, JST, M-31 Suite 6A, 6-31-15 Jingumae, Shibuya-ku, Tokyo 150-0001, Japan 2 Department of Information Science, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan 3 Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan 4 Sony Computer Science...

Shi and Tsai (JRSSB, 2002) proposed an interesting residual information criterion (RIC) for model selection in regression. Their RIC was motivated by the principle of minimizing the Kullback-Leibler discrepancy between the residual likelihoods of the true and candidate model. We show, however, under this principle, RIC would always choose the full (saturated) model. The residual likelihood ther...

Inferring population structure using bayesian clustering programs often requires a priori specification of the number of subpopulations, K, from which the sample has been drawn. Here, we explore the utility of a common bayesian model selection criterion, the Deviance Information Criterion (DIC), for estimating K. We evaluate the accuracy of DIC, as well as other popular approaches, on datasets ...