The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
—Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
This paper presents a Bayesian approach to achieve efficient and accurate motion tracking in monocular image sequences. We first extract a deterministic motion model with six degr...
Bayesian networks (BN) constitute a useful tool to model the joint distribution of a set of random variables of interest. To deal with the problem of learning sensible BN models fr...