Continuous quantities are ubiquitous in models of real-world phenomena, but are
surprisingly difficult to reason about automatically. Probabilistic graphical models such as …

The popularity of particle filtering for inference in Markov chain models defined over random
variables with very large or continuous domains makes it natural to consider sample–based …

One of the major obstacles to using Bayesian methods for pattern recognition has been its
computational expense. This thesis presents an approximation technique that can perform …

Bayesian filtering is a general framework for recursively estimating the state of a dynamical
system. Key components of each Bayes filter are probabilistic prediction and observation …

In many applications of graphical models arising in computer vision, the hidden variables of
interest are most naturally specified by continuous, non-Gaussian distributions. There exist …

The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative
to the extended Kalman filter and the particle filter. Here, we extend the SP filter to …

Bayesian inference can be embedded into an appropriately defined dynamics in the space
of probability measures. In this paper, we take Brownian motion and its associated Fokker …

We develop statistical methods which allow effective visual detection, categorization, and
tracking of objects in complex scenes. Such computer vision systems must be robust to wide …

Exact monitoring in dynamic Bayesian networks is intractable, so approximate algorithms
are necessary. This paper presents a new family of approximate monitoring algorithms that …

This article describes a robust algorithm to estimate a conditional probability density f (t| x)
as a non-parametric smooth regression function. It is based on a neural network and the …