Monte Carlo Methods for Stochastic Differential Equations and their Applications
We introduce computationally efficient Monte Carlo methods for studying the statistics of stochastic differential equations in two distinct settings. In the first, we derive importance sampling methods for data assimilation when the noise in the model and observations are small. The methods are form...
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| Language: | en_US |
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The University of Arizona.
2017
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| Online Access: | http://hdl.handle.net/10150/624570 http://arizona.openrepository.com/arizona/handle/10150/624570 |