We describe a formal framework for analyzing how statistical properties of natural environments and the process of natural selection interact to determine the design of perceptual and cognitive systems. The framework consists of two parts: a Bayesian ideal observer with a utility function appropriate for natural selection, and a Bayesian formulation of Darwin’s theory of natural selection. Simu...

We propose a semi-supervised bootstrapping algorithm for analyzing China’s foreign relations from the People’s Daily. Our approach addresses sentiment target clustering, subjective lexicons extraction and sentiment prediction in a unified framework. Different from existing algorithms in the literature, time information is considered in our algorithm through a hierarchical bayesian model to guid...

BANANA provides a graphical connection of these 12 factors with 26 arcs, showing that: a) Defense ability (DA) is the major factor in survival (with 8 arcs constraints); b) Energy lost in fighting (LA) is the second important factor (with seven arcs constraints); c) The relationships and dependencies also indicate that speedy attack ability (SA), and the energy cost of survival (EF) (with six a...

This paper presents a simple logical framework for abduction, with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straight-forward implementations using branch and bound search with either logic-programming technology or ATMS technology. The main focus of this paper is arguing for a form of representational adequacy of this very simple system f...

Obesity has become one of the most prevalent health problems around the world. Many obesity therapy cases need efficient management in order to be shared and utilized. Prescription management has been proved to be successful strategy in obesity management. Since a case usually contains incomplete information, this article examines probabilistic case-based reasoning (CBR) by integrating Bayesian...

While the classical framework has a rich set of limited information procedures such as GMM and other related methods, the situation is not so in the Bayesian framework. We develop a limited information procedure in the Bayesian framework that does not require the knowledge of the full likelihood. The developed procedure is a Bayesian counterpart of the classical GMM but has advantages over the ...

We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem is the generative framework, which is the basis of solutions such as the Bayesian algorithm and its approximation, the particle filters. However, these solutions can be very sensitive to model mismatches. In this paper...

The authors consider the correlation between two arbitrary functions of the data and a parameter, when the latter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p-values and posterior proba...

Conventional approaches to content-based image retrieval exploit low-level visual information to represent images and relevance feedback techniques to incorporate human knowledge into the retrieval process, which can only alleviate the semantic gap to some extent. To further boost the performance, a Bayesian framework is proposed in which information independent of the visual content of images ...

The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models of perception, and use this observation to frame a new computational account of the need for, and action ...