Under a Bayesian approach to a hierarchical model interest often lies in summarizing the posterior distribution of a parameter in order to enable easy comparison across prior structures This can be done for example through quantile or interval estimation When using an MCMC algorithm such as the Gibbs sampler to generate a sample from the posterior of interest calcu lations are often easier when...

Visual tracking of multiple objects is a key component of many visual-based systems. While there are reliable algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple interacting objects that have a complex dynamics. In this article, a novel Bayesian model for tracking multiple interacting objects in...

A class of semi-parametric hazard/failure rates with a bathtub shape is of interest. It does not only provide a great deal of flexibility over existing parametric methods in the modeling aspect but also results in a closed and tractable Bayes estimator for the bathtub-shaped failure rate (BFR). Such an estimator is derived to be a finite sum over two S-paths due to an explicit posterior analysi...

Markov chain Monte Carlo (MCMC) methods for Bayesian computation are mostly used when the dominating measure is the Lebesgue measure, the counting measure, or a product of these. Many Bayesian problems give rise to distributions that are not dominated by the Lebesgue measure or the counting measure alone. In this article we introduce a simple framework for using MCMC algorithms in Bayesian comp...

Approximate inference by sampling from an appropriately constructed posterior has recently seen a dramatic increase in popularity in both the robotics and computer vision community. In this paper, I will describe a number of approaches in which my co-authors and I have used Sequential Monte Carlo methods and Markov chain Monte Carlo sampling to solve a variety of difficult and challenging infer...

Abstract: A new method for 3D reconstruction from dynamic stereo image by using state estimation with particle filter is proposed. Associations of feature points between two images and 3D position of the feature points are estimated simultaneously by particle filter. It is due to the applicability of particle filter for nonlinear and non-Gaussian state space model including unknown associations...