Name
Abstract | ||
|---|---|---|
Spatial statistical models are of considerable practical and theoretical interest. However, there has been little work on rare-event probability estimation for such models. In this paper we present a conditional Monte Carlo algorithm for the estimation of the probability that random graphs related to Bernoulli and continuum percolation are connected. Numerical results are presented showing that the conditional Monte Carlo estimators significantly outperform the crude simulation estimators.
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Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WSC.2014.7019916 | WSC '14: Winter Simulation Conference
Savannah
Georgia
December, 2014 |
Keywords | Field | DocType |
Monte Carlo methods,estimation theory,graph theory,probability,statistical analysis,Bernoulli percolation,conditional Monte Carlo algorithm,continuum percolation,random graph connectivity,rare event probability estimation,spatial statistical models | Statistical physics,Monte Carlo method,Random graph,Conditional probability distribution,Markov chain Monte Carlo,Simulation,Hybrid Monte Carlo,Quantile function,Regular conditional probability,Monte Carlo integration,Statistics,Mathematics | Conference |
ISSN | ISBN | Citations |
0891-7736 | 978-1-4673-9741-4 | 1 |
PageRank | References | Authors |
0.37 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rohan Shah | 1 | 1 | 1.05 |
| C. Hirsch | 2 | 8 | 4.31 |
| Dirk P. Kroese | 3 | 429 | 37.56 |
| Volker Schmidt | 4 | 7 | 4.80 |