We present a new technique called Multiple Vertex Next Event Estimation, which outperforms current direct lighting techniques in forward scattering, optically dense media with the Henyey-Greenstein phase function. Instead of a one-segment connection from a vertex within the medium to the light source, an entire sub path of arbitrary length can be created and we show experimentally that 4-10 seg...

In this paper we propose a fast adaptive Importance Sampling method for the efficient simulation of buffer overffow probabilities in queueing networks. The method comprises three stages. First we estimate the minimum Cross-Entropy tilting parameter for a small buffer level; next, we use this as a starting value for the estimation of the optimal tilting parameter for the actual (large) buffer le...

which can be shown to be equivalent to MMDH(q, p) 2 = Ex,x′∼p[k(x, x′)]− 2Ex∼p;y∼q[k(x, y)] + Ey,y′∼q[k(y, y′)]. We show that kernelized discrepancy is equivalent to MMDHp(q, p), equipped with the p-Steinalized kernel kp(x, x ′). Proposition 1.1. Assume (3) is true, we have S(q, p) = MMDHp(q, p). Proof. Simply note that Ex′∼p[kp(x, x′)] = 0 for any x, we have MMDHp(q, p) 2 = Ex,x′∼q[kp(x, x′)] ...

We provide a comparative study of several widely used off-policy estimators (Empirical Average, Basic Importance Sampling and Normalized Importance Sampling), detailing the different regimes where they are individually suboptimal. We then exhibit properties optimal estimators should possess. In the case where examples have been gathered using multiple policies, we show that fused estimators dom...

Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated. We address this problem by studying black-box importance sampling methods that calculate importance weights for samples generated from any unknown proposal or black-box mechanism. Our method all...

We present algorithms for Bayesian learning of decomposable models from data. Priors of a certain form admit exact averaging in O(3nn3) time and sampling T graphs from the posterior in O(4n + nT) time. To target a broader class of priors we associate each sample with an importance weight. Empirically, we compare averaging to optimization and demonstrate the accuracy of our importance sampling e...

KATIE is a parton-level event generator for hadron scattering processes that can deal with partonic initial-state momenta with an explicit transverse momentum dependence causing them to be space-like. Provided with the necessary transverse momentum dependent parton density functions, it calculates the tree-level off-shell matrix elements and performs the phase space importance sampling to produ...

Abstract: In this paper we consider estimation of R = P (Y < X), when X and Y are distributed as two independent four-parameter generalized gamma random variables with same location and scale parameters. A modified maximum likelihood method and a Bayesian technique have been used to estimate R on the basis of independent samples. As the Bayes estimator cannot be obtained in a closed form, it ha...

The concept of simulating a variant of QCD with sea quarks which interact with the gluon configuration only via global gluonic quantities like ∫ FF̃ dx and ∫ FF dx is tested for the case of the massive 2-flavour Schwinger model. It is found to amount to an importance sampling method which generates –at essentially the costs of a quenched run– an ensemble in between a full and a quenched one, thu...

The determination of packet loss rates in multiplexers is an important problem that has received much attention in the literature. This paper considers the problem of estimating, via simulation, extremely low packet loss rates in a voice-data multiplexer. The multiplexer gives priority to voice packets, unless the data queue length exceeds a threshold. To eeciently simulate such low loss rates ...