A synopsis of Monte Carlo perturbation algorithms
Fundamental aspects of correlated sampling and differential operator procedures applied to integrals and systems of linear equations modelling Markov processes are investigated. Algorithms providing sensitivities (gradients, Jacobians) and perturbation estimates obtained by a single simulation experiment are described in detail and explained by examples. Mathematical proofs are provided which show that under most conditions a finite relative variance can be obtained for arbitrarily small parameter variations.
Bibliographic Reference: Article: Journal of Computational Physics, Vol. III (1994) No. 1, pp. 33-48
Record Number: 199410917 / Last updated on: 1994-11-28
Original language: en
Available languages: en