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Bayesian non-parametric analysis of diffusions

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If you have a question about this talk, please contact Richard Nickl.

This presentation will describe an approach to Bayesian non-parametric analysis of diffusion drift functions. Gaussian processes can be used as conjugate priors, and we describe methodology for characterising posterior mean and covariance structure in terms of solutions to differential equations with coefficients given as functions of the observed diffusion local time. The talk will mainly cover the continuously observed sample path case, though recent work on the discretely observed case, where diffusion coefficient estimation is also an important issue, will also be outlined. This is joint work with Omiros Papaspiliopoulos, Yvo Pokern and Andrew Stuart.

This talk is part of the Statistics series.

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