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Bayesian inference in continuous time jump processes

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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.

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