University of Birmingham > Talks@bham > Data Science and Computational Statistics Seminar > Stochastic optimal control and importance sampling for diffusion processes

Stochastic optimal control and importance sampling for diffusion processes

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Diffusion processes described by stochastic differential equations play an important role in many applications, such as molecular dynamics. In these applications, one often aims to estimate the expected value of a path functional with respect to the law of the diffusion process. One approach to this problem involves importance sampling via a change of measure, where the optimal change of measure is unique and yields a zero-variance estimator. Finding the optimal change of measure can be formulated as a stochastic optimal control problem. These problems can be solved computationally using methods based on gradient descent. We analyse a class of stochastic optimal control problems with the aim of understanding the convergence of gradient descent-based methods.

This talk is part of the Data Science and Computational Statistics Seminar series.

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