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 ...

We present a Bayesian blackboard system for temporal perception, applied to a minidomain task in musical scene analysis. It is similar to the classic Copycat architecture (Hofstadter 1995) but is derived from rigourous modern Bayesian network theory (Bishop 2006), with heuristics added for speed. It borrows ideas of priming, pruning and attention and fuses them with modern inference algorithms,...

Online social networks create significant challenges to computer scientists, physicists, and sociologists alike, for their massive size, fast evolution, and uncharted potential for social computing. One particular problem that has interested us is community identification. In this review, we focus on “community detection in social networks” through different approaches and techniques mainly Bay...

Recent results suggest that quantum mechanical phenomena may be interpreted as a failure of standard probability theory and may be described by a Bayesian complex probability theory.

There is a certain excitement in vision science concerning the idea of applying the tools of Bayesian decision theory to explain our perceptual capacities. Bayesian models – or ‘predictive coding models’ – are thought to be needed to explain how the inverse problem of perception is solved, and to rescue a certain constructivist and Kantian way of understanding the perceptual process (Clark 2012...

One of the most troubling and persistent challenges for Bayesian Confirmation Theory is the Problem of Old Evidence (POE, Glymour 1980). The problem arises for anyone who wants to model confirmation and theory appraisal in science by means of Bayesian Conditionalization. This paper addresses the problem as follows: First, I clarify the nature and the varieties of the POE, following Eells (1985,...

This papers investigates the manipulation of statements of strong independence in probabilistic logic. Inference methods based on polynomial programming are presented for strong independence, both for unconditional and conditional cases. We also consider graph-theoretic representations, where each node in a graph is associated with a Boolean variable and edges carry a Markov condition. The resu...

A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a binary classifier. The theory of rough sets provides a ternary classification method by approximating a set into positive, negative and boundary regions based on an equivalence relation on the universe. In this paper, ...