Simulation of conditional diffusions via forward-reverse stochastic representations

Type
Presentation

Authors
Bayer, Christian

Date
2015-01-07

Abstract
We derive stochastic representations for the finite dimensional distributions of a multidimensional diffusion on a fixed time interval,conditioned on the terminal state. The conditioning can be with respect to a fixed measurement point or more generally with respect to some subset. The representations rely on a reverse process connected with the given (forward) diffusion as introduced by Milstein, Schoenmakers and Spokoiny in the context of density estimation. The corresponding Monte Carlo estimators have essentially root-N accuracy, and hence they do not suffer from the curse of dimensionality. We also present an application in statistics, in the context of the EM algorithm.

Conference/Event Name
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)

Additional Links
http://mediasite.kaust.edu.sa/Mediasite/Play/9754ebb0576e4ae5957ef24385f8cebb1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52

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