Locally adaptive Monte Carlo methods
Duration: 31 mins 13 secs
Share this media item:
Embed this media item:
Embed this media item:
About this item
| Description: |
Lee, A (University of Warwick)
Tuesday 22 April 2014, 15:50-16:25 |
|---|
| Created: | 2014-04-28 14:48 |
|---|---|
| Collection: | Advanced Monte Carlo Methods for Complex Inference Problems |
| Publisher: | Isaac Newton Institute |
| Copyright: | Lee, A |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
|
| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-authors: Christophe Andrieu (University of Bristol), Arnaud Doucet (University of Oxford)
In various situations of interest, natural implementations of Monte Carlo algorithms such as Markov chain Monte Carlo and sequential Monte Carlo can perform poorly due to uneven performance in different parts of the space in which they operate. For example, in Markov chain Monte Carlo a Markov kernel may behave increasingly poorly in the tails of the target distribution of interest and in sequential Monte Carlo the quality of associated estimates may plummet if too few particles are used at a particular time. We overview a particular strategy, local adaptation, that seeks to overcome some of these phenomena in practice. |
|---|---|
Available Formats
| Format | Quality | Bitrate | Size | |||
|---|---|---|---|---|---|---|
| MPEG-4 Video | 640x360 | 1.93 Mbits/sec | 454.05 MB | View | Download | |
| WebM | 640x360 | 539.85 kbits/sec | 123.50 MB | View | Download | |
| iPod Video | 480x270 | 521.99 kbits/sec | 119.35 MB | View | Download | |
| MP3 | 44100 Hz | 249.77 kbits/sec | 57.17 MB | Listen | Download | |
| Auto * | (Allows browser to choose a format it supports) | |||||