Efficient Monte Carlo resampling for probability measure changes from Bayesian updating

Optimal Monte Carlo updating.

Based on Peskun's theorem it is shown that optimal transition matrices in Markov chain Monte Carlo should have zero diagonal elements except for the diagonal element corresponding to the largest weight. We will compare the statistical efficiency of this sampler to existing algorithms, such as heat-bath updating and the Metropolis algorithm. We provide numerical results for the Potts model as an...

Forest resampling for distributed sequential Monte Carlo

This paper brings explicit considerations of distributed computing architectures and data structures into the rigorous design of Sequential Monte Carlo (SMC) methods. A theoretical result established recently by the authors shows that adapting interaction between particles to suitably control the Effective Sample Size (ESS) is sufficient to guarantee stability of SMC algorithms. Our objective i...

Probability: From Monte Carlo to Geometry

Neural-bayesian Filtering Based on Monte Carlo Resampling for Visual Robust Tracking

The visual robust tracking is an acid test for existing methods since the target with large-dynamic-change scenarios. Specifically, this paper presents neural aided Bayesian filtering scheme which is based on Monte Carlo resampling techniques associated with lower particles hypothesis to addresses the computational intensity that is intrinsic to all particle filter (PF) approaches, including th...

Bayesian Monte Carlo

We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation of prior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method. We also attempt more challenging multidimensional integrals involved in computi...