The well-known variance reduction methods—the method of importance sampling and the method of control variates—can be exploited if an approximation of the required solution is known. Here we employ conditional probabilistic representations of solutions together with the regression method to obtain sufficiently inexpensive (although rather rough) estimates of the solution and its derivatives by ...

In the Kalman filter, the state dynamics is specified by the state equation while the measurement equation characterizes the likelihood. In this paper, we propose a generalized methodology of specifying state dynamics using the conditional density of the states given its neighbors without explicitly defining the state equation. In other words, the typically strict linear constraint on the state...

A recent technique that forms virtual ray lights (VRLs) from path segments in media, reduces the artifacts common to VPL approaches in participating media, however, distracting singularities still remain. We present Virtual Beam Lights (VBLs), a progressive many-lights algorithm for rendering complex indirect transport paths in, from, and to media. VBLs are efficient and can handle heterogeneou...

A sophisticated approach for computing the total economic capital needed for various stochastically dependent risk types is the bottom-up approach. In this approach, usually, market and credit risks of financial instruments are modeled simultaneously. As integrating market risk factors into standard credit portfolio models increases the computational burden of calculating risk measures, it is a...

We present techniques for importance sampling from distributions defined by Relational Bayesian Networks. The methods operate directly on the abstract representation language, and therefore can be applied in situations where sampling from a standard Bayesian Network representation is infeasible. We describe experimental results from using standard, adaptive and backward sampling strategies. Fur...

In this paper, we propose an importance-sampling based method to obtain an unbiased simulator to evaluate expectations involving random variables whose probability density functions are unknown while their Fourier transforms have an explicit form. We give a general principle about how to choose appropriate importance samplers under different models. Compared with the existing methods, our metho...

This article proposes an approach for visual track ing using multiple cameras with overlapping fields of view. A spatial and temporal recursive Bayesian filtering approach using particle filter is proposed to fuse image sequences of multiple cameras to optimally estimate the state of the system, i.e., the target’s location. An approximation method for importance sampling function and weight upd...

In this paper we study the rare event of overflow in a Markov fluid queue with finite buffer and many input sources. The probability of this rare event will be estimated by simulations. We present a highly efficient importance sampling procedure to speed up the simulations. The implemented change of meausure is suggested after a large deviations analysis of the overflow probability. This analys...

Recent progress in deep latent variable models has largely been driven by the development of flexible and scalable variational inference methods. Variational training of this type involves maximizing a lower bound on the log-likelihood, using samples from the variational posterior to compute the required gradients. Recently, Burda et al. (2015) have derived a tighter lower bound using a multi-s...

Surles and Padgett [15] introduced two-parameter Burr Type X distribution, which can be described as a generalized Rayleigh distribution. In this paper we consider the estimation of the stress-strength parameter R = P [Y < X], when X and Y are both three-parameter generalized Rayleigh distribution with the same scale and locations parameters but different shape parameters. It is assumed that th...