The calculation of value-at-risk (VAR) for large portfolios of complex instruments is among the most demanding and widespread computational challenges facing the financial industr...
Paul Glasserman, Philip Heidelberger, Perwez Shaha...
In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and...
Anton J. Kleywegt, Alexander Shapiro, Tito Homem-d...
We describe a physically-based Monte Carlo technique for approximating bidirectional reflectance distribution functions (BRDFs) for a large class of geometries by directly simulat...
Stephen H. Westin, James Arvo, Kenneth E. Torrance
This paper presents a novel discrete population based stochastic optimization algorithm inspired from weed colonization. Its performance in a discrete benchmark, timecost trade-off...
Bayesian estimation in nonlinear stochastic dynamical systems has been addressed for a long time. Among other solutions, Particle Filtering (PF) algorithms propagate in time a Mon...