We propose a framework for optimization problems based on particle filtering (also called Sequential Monte Carlo method). This framework unifies and provides new insight into rand...
This paper compares Monte Carlo methods, lattice rules, and other low-discrepancy point sets on the problem of evaluating asian options. The combination of these methods with vari...
Under certain conditions on the integrand, quasi-Monte Carlo methods for estimating integrals (expectations) converge faster asymptotically than Monte Carlo methods. Motivated by ...
Shane G. Henderson, Belinda A. Chiera, Roger M. Co...
We consider a new simulation-based optimization method called the Nested Partitions (NP) method. This method generates a Markov chain and solving the optimization problem is equiv...
We present two parallel algorithms and their Unified Parallel C implementations for Bayesian indoor positioning systems. Our approaches are founded on Markov Chain Monte Carlo si...