University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Bayesian inference in continuous time jump processes

Bayesian inference in continuous time jump processes

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

If you have a question about this talk, please contact Mustapha Amrani.

Inference for Change-Point and Related Processes

In this talk I will discuss recent advances in inference for continuous time processes with random changepoints or jumps. I will discuss cases with finite numbers of jumps, modelled within a jump-diffusion or piecewise deterministic processed framework, then go on to describe processes with almost surely infinite numbers of jumps on finite intervals, focussing on recent developments for alpha-stable Levy processes. Methodology is Bayesian, using computational methods related to Markov chain Monte Carlo and particle filtering.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2021 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity