The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals and in estimating the null distribution of test statistics. This paper builds on the work of Chen et al. (2005), providing an intuitive explanation of the s...

We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the disparity map has been computed, and suppose that the only information available is the stereoscopic pair. The method, which consists of sampling from the posterior distribution given the stereoscopic pair, allows the ...

This research article presents, a blended two-sided chain inspection plan with process potential measure. The Probability of acceptance and related measures are shown. Tables prepared to find the parameters plan. In this variable sample size is obtained by using normal distribution in attribute inspection, sampling which yields small used designed really production industries study product resp...

OBJECTIVE To examine the effectiveness of methods to improve partner notification by patient referral (index patient has responsibility for informing sex partners of their exposure to a sexually transmitted infection). DESIGN Systematic review of randomised trials of any intervention to supplement simple patient referral. DATA SOURCES Seven electronic databases searched (January 1990 to Dec...

We present a general method for proving rigorous, a priori bounds on the number of iterations required to achieve convergence of Markov chain Monte Carlo. We describe bounds for spe-ciic models of the Gibbs sampler, which have been obtained from the general method. We discuss possibilities for obtaining bounds more generally.

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimens...

The naive importance sampling estimator based on the samples from a single importance density can be extremely numerically unstable. We consider multiple distributions importance sampling estimators where samples from more than one probability distributions are combined to consistently estimate means with respect to given target distributions. These generalized importance sampling estimators pr...

Stochastic Optimality Theory (Boersma, 1997) is a widely-used model in linguistics that did not have a theoretically sound learning method previously. In this paper, a Markov chain Monte-Carlo method is proposed for learning Stochastic OT Grammars. Following a Bayesian framework, the goal is finding the posterior distribution of the grammar given the relative frequencies of input-output pairs. ...