Event-chain algorithms: taming randomness in Monte Carlo methods through irreversibility, factorization and lifting
39 mins 44 secs,
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About this item
| Description: |
Manon, M
Tuesday 18th July 2017 - 12:10 to 12:50 |
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| Created: | 2017-07-19 12:12 |
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| Collection: | Scalable inference; statistical, algorithmic, computational aspects |
| Publisher: | Isaac Newton Institute |
| Copyright: | Manon, M |
| 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: | I will first present the irreversible and rejection-free Monte Carlo methods recently developed in Physics under the name Event-Chain. They have proven to produce clear acceleration over standard Monte Carlo methods, thanks to the reduction of their random-walk behavior. Their irreversible nature relies on three key ingredients: the factorized filter, the generalized lifting framework and the infinitesimal moves. Then, I will focus on the new Forward Event-Chain version that allows to reduce the randomization needed for ergodicity, leading to a striking speed-up. Finally, I will explain how the factorized filter may be the key to subsampling in Monte Carlo methods. |
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