In this paper, a method is presented for the efficient estimation of rare-event (overflow) probabilities in Jackson queueing networks using importance sampling. The method differs in two ways from methods discussed in most earlier literature: the change of measure is state-dependent, i.e., it is a function of the content of the buffers, and the change of measure is determined using a cross-entr...

A probability is the expectation of an indicator function. However, the standard pathwise sensitivity estimation approach, which interchanges the differentiation and expectation, cannot be directly applied because the indicator function is discontinuous. In this paper, we design a pathwise sensitivity estimator for probability functions based on a result of Hong [Hong, L. J. 2009. Estimating qu...

The problem of noise in Monte-Carlo rendering arising from estimator variance is well-known and well-studied. In this work, we concentrate on identifying individual light paths as outliers that lead to significant spikes of noise and represent a challenge for existing filtering methods. Most noise-reduction methods, such as importance sampling and stratification, attempt to generate samples tha...

Recently, some authors have suggested usage models of Markov type as a technique of specifying the estimated operational use distribution of a given program. A main purpose of such models is the derivation of random test cases allowing unbiased estimates on the (un)reliability of the program in its intended environment. In this article, we show that by a shift of the transition probabilities of...

We present a method to obtain stateand time-dependent importance sampling estimators by repeatedly solving a minimum cross-entropy (MCE) program as the simulation progresses. This MCE-based approach lends a foundation to the natural notion to stop changing the measure when it is no longer needed. We use this method to obtain a stateand time-dependent estimator for the one-tailed probability of ...

This paper considers importance sampling as a tool for rareevent simulation. The focus is on estimating the probability of overflow in the downstream queue of a Jackson twonode tandem queue. It is known that in this setting ‘traditional’ state-independent importance-sampling distributions perform poorly. We therefore concentrate on developing a state-dependent change of measure that is provably...

Abstract: The basic idea of importance sampling is to use independent samples from one measure in order to approximate expectations with respect to another measure. Understanding how many samples are needed is key to understanding the computational complexity of the method, and hence to understanding when it will be effective and when it will not. It is intuitive that the size of the difference...

Causal inference in cue combination is to decide whether the cues have a single cause or multiple causes. Although the Bayesian causal inference model explains the problem of causal inference in cue combination successfully, how causal inference in cue combination could be implemented by neural circuits, is unclear. The existing method based on calculating log posterior ratio with variable elim...

Problem definition: The input is a DNF formula f with m clauses C1, . . . , Cm over n variables x1, . . . , xn, i.e. f = ∨i=1Ci where each Ci is the conjunction of literals like x2 ∧ x̄5 ∧ xn. The goal is the estimate the number of Boolean assignments that satisfy f . Theorem 1 [Karp and Luby, 1983]: Let S ⊂ {0, 1}n be the set of satisfying assignments. Then |S| can be estimated within factor 1 ...

This paper presents a method for compressing measured datasets of the near-field emission of physical light sources (represented by raysets). We create a mesh on the bounding surface of the light source that stores illumination information. The mesh is augmented with information about directional distribution and energy density. We have developed a new approach to smoothly generate random sampl...