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...

Current estimates of diversifying positive selection rely on first having an accurate multiple sequence alignment. Simulation studies have shown that under biologically plausible conditions, relying on a single estimate of the alignment from commonly used alignment software can lead to unacceptably high false-positive rates in detecting diversifying positive selection. We present a novel statis...

We construct Markov chain algorithms for sampling from discrete exponential families conditional on a sufticient statistic. Examples include contingency tables, logistic regression, and spectral analysis of permutation data. The algorithms involve computations in polynomial rings using Grobner bases. 1. Introduction. This paper describes new algorithms for sampling from the conditional distribu...

Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator...

In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new observations are received. Since a single particle tracks many states, this filter can...