Speaker |
Sam Livingstone |
---|---|
Affiliation |
UCL, Statistics |
Date |
Friday, 01 November 2013 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Malet Place Engineering 1.03 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
The Metropolis-adjusted Langevin algorithm (MALA) and manifold-variant (MMALA) are two Markov chain Monte Carlo methods based on diffusions. While theoretical properties of the former are better understood, the latter has appeared more effective in practice, producing more efficient estimates for the same computational budget in many experiments (e.g. Girolami & Calderhead, 2011). The focus of this talk will be to highlight two properties of the diffusion on which MMALA is based, which suggest that a slightly different diffusion would prove a better basis for MCMC, both in terms of proposal choice and speed of computation. This is joint work with Chris Sherlock & Tatiana Xifara (Lancaster), and Simon Byrne & Mark Girolami (UCL). Slides for the talk: PDF |
Biography |