Facial behaviors represent activities of face or facial feature in spatial or temporal space, such as facial expressions, face pose, gaze, and furrow happenings. An automated system for facial behavior recognition is always desirable. However, it is a challenging task due to the richness and ambiguity in daily facial behaviors. This paper presents an efficient approach to real-world facial beha...

Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions , or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current f...

This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We propose approximate inference algorithms that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Particle Filte...

Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approximate inference involves grouping the variables in the process into smaller factors and keeping independent beliefs over these factors. In this paper we present several techniques for decomposing a dynamic Bayesian net...

This paper presents a new deterministic approximation technique in Bayesian networks. This method, “Expectation Propagation,” unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks. Loopy belief propagation, because it propagates exact belief states, is useful for a limi...

Special-case algorithms for Bayesian belief networks are designed to alleviate the computational burden of problem solving. These algorithms provide a case base for storing solutions for a small number of situations that are likely to be encountered during problem solving. This case base is employed as a lter for belief-network inference: for a problem under consideration, the network at hand i...

MASTS Annual Science Meeting Title: Additional insight on potential reallocation scenarios for artisanal fisheries from a spatialBayesian belief network

Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidden units. The Indian buffet process has been used as a nonparametric Bayesian prior on the structure of a directed belief network with a single infinitely wide hidden layer. Here, we introduce the cascading Indian buff...