Bayesian inference in continuous time jump processes
Duration: 44 mins 32 secs
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About this item
| Description: |
Godsill, S (University of Cambridge)
Thursday 16 January 2014, 13:30-14:15 |
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| Created: | 2014-01-24 11:55 |
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| Collection: | Inference for Change-Point and Related Processes |
| Publisher: | Isaac Newton Institute |
| Copyright: | Godsill, S |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | 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. |
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