Lightness constancy is the remarkable ability of human observers to perceive surface reflectance accurately despite variations in illumination and context. Two successful approaches to understanding lightness perception that have developed along independent paths are anchoring theory and Bayesian theories. Anchoring theory is a set of rules that predict lightness percepts under a wide range of ...

Whilst meta-analysis is becoming a more commonplace statistical technique, Bayesian inference in meta-analysis requires complex computational techniques to be routinely applied. We consider simple approximations for the first and second moments of the parameters of a Bayesian random effects model for meta-analysis. These computationally inexpensive methods are based on simple analytical formula...

Multiple integrals encountered when evaluating posterior densities in Bayesian estimation were approximated using Multivariate Gaussian Quadrature (MGQ). Experimental results for a linear regression model suggest MGQ provides better approximations to unknown parameters and error variance than simple Monte Carlo based approximations. The authors are Graduate Research Assistant, Professor and Gra...

Agencies consisting of simple agents can be deployed on the Internet to solve problems that are too complex for single autonomous Internet agents. We deploy Bayesian agencies on the Internet to achieve emergent belief propagation through the collective behavior of simple agents organized into heterarchies of agencies. Collectively the agents use probabilistic reasoning to deal with the uncertai...

In the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of "uninformative" prior distribution intended to have minimal inferential influence. Bayes rule will still produce nice-looking estimates and credible intervals, but these lack the logical force attached to experience-based priors and require further justification. This paper concer...

What simple learning rules can allow agents to cope with changing environments? We tested whether a rule that neglects base rates of events in the world — something that is usually considered irrational — could be as successful as Bayesian inference — the usual standard of rationality — in making cuebased predictions about events in time-varying environments. We focused on environments in which...

The use of the conjugacy property for members of the exponential family of distributions is commonplace within Bayesian statistical analysis, allowing for tractable and simple solutions to problems of inference. However, despite a shared motivation, there has been little previous development of a similar property for using utility functions within a Bayesian decision analysis. As such, this art...

In this article a new probabilistic information retrieval (IR) model, based on Bayesian networks (BNs), is proposed. We first consider a basic model, which represents only direct relationships between the documents in the collection and the terms or keywords used to index them. Next, we study two versions of an extended model, which also represents direct relationships between documents. In eit...

The relative contributions of fat-free mass (FFM) and fat mass (FM) to body weight are key indicators for several major public health issues. Predictive models could offer new insights into body composition analysis. A non-parametric equation derived from a probabilistic Bayesian network (BN) was established by including sex, age, body weight and height. We hypothesised that it would be possibl...

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. In this paper, we discuss an approach to collaborative filtering based on the Simple Bayesian Classifier, and apply our model to two variants of the collaborative fi...