In this paper we examine a control variate estimator for quantity that can be expressed as the expectation of functional random process, is itself solution differential equation driven by fast mean-reverting ergodic forces. The same limit diffusion process approximates original when mean-reversion time goes to zero. To get an efficient estimator, propose coupling method build and process. We sh...

We prove that any pair of bivariate trinomials has at most 5 isolated roots in the positive quadrant. The best previous upper bounds independent of the polynomial degrees were much larger, e.g., 248832 (for just the non-degenerate roots) via a famous general result of Khovanski. Our bound is sharp, allows real exponents, allows degeneracies, and extends to certain systems of n-variate fewnomial...

This article presents a new method for contour detection in high-resolution polarimetric SAR images. The method is based on multi-variate statistics of speckle in homogeneous regions in a SAR image and uses a hypothesis test for the difference in variance between two adjacent regions to find the contours. The detector is directly applied on the single-look complex polarimetric SAR image. A prep...

Simulation is an essential tool for performance evaluation of communication networks. We are interested in the waiting timeW of packets. The Control Variates method takes profit of the knowledge about another stochastic process strongly correlated withW to reduce the uncertainty in the estimation of its mean. We analyze the usefulness of the cycle time as a control stochastic process for Medium...

Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control variate method to effectively reduce variance for policy gradient methods. Motivated by the Stein’s...

In 1952, von Neumann introduced the rejection method for random variate generation. We revisit this algorithm when we have a source of perfect bits at our disposal. In this random bit model, there are universal lower bounds for generating a random variate with a given density to within an accuracy derived by Knuth and Yao, and refined by the authors. In general, von Neumann’s method fails in th...

We present Bayesian analyses of matrix-variate normal data with conditional independencies induced by graphical model structuring of the characterizing covariance matrix parameters. This framework of matrix normal graphical models includes prior specifications, posterior computation using Markov chain Monte Carlo methods, evaluation of graphical model uncertainty and model structure search. Ext...