Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
Simple Markov-chain algorithms for generating bipartite graphs and tournaments
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Generating random regular graphs
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Sampling binary contingency tables with a greedy start
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Asymptotic enumeration of sparse 0-1 matrices with irregular row and column sums
Journal of Combinatorial Theory Series A
Introduction to Rare Event Simulation
Introduction to Rare Event Simulation
Importance sampling in rate-sharing networks
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
| Hi-index | 0.00 |
Large deviations analysis for light-tailed systems provides anasymptotic description of the optimal importance sampler in thescaling of the Law of Large Numbers. As we will show by means of asimple example related to computational finance, such asymptoticdescription can be interpreted indifferent ways suggesting severalimportance sampling algorithms, some of them state-dependent. Inturn, the performance of the suggested algorithms can besubstantially different.