University of Cambridge > > Statistics > Continuous-time Importance Sampling for Multivariate Diffusions

Continuous-time Importance Sampling for Multivariate Diffusions

Add to your list(s) Download to your calendar using vCal

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

Inference for multivariate diffusion processes is challenging due to the intractability of the dynamics of the process. Most methods rely on high frequency imputation and discrete-time approximations of the continuous-time model, leading to biased inference. Recently, methods that are able to perform inference for univariate diffusions which avoid time-discretisation errors have been developed. However these approaches cannot be applied to general multivariate diffusions.

Here we present a novel, continuous-time Importance Sampling method that enables inference for general continuous-time Markov processes whilst avoiding time-discretisation errors. The method can be derived as a limiting case of a discrete-time sequential importance sampler, and uses ideas from random-weight particle filters, retrospective sampling and Rao-Blackwellisation.

Joint work with Gareth Roberts, Giorgos Sermaidis and Krys Latuszynski.

This talk is part of the Statistics series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2023, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity