Non UBC
DSpace
Roberts, Gareth 0.
2019-05-13T09:08:56Z
2018-11-13T11:13
The presentation will review results on infinite dimensional limits for some modern MCMC algorithms with a particular focus on Piecewise Deterministic Markov Processes PDMPs. The talk will also discuss the methodological consequences of these results for MCMC implementation.
For certain stylised sequences of target density and particular MCMC algorithms, limit results can be obtained as the dimension of the target diverges. For traditional (Metropolis-Hastings type) MCMC algorithms, such limits are typically (but not always) diffusions. For non-reversible alternatives such as PDMPs similar results can be obtained, although often not of diffusion form and not even Markov. The two simplest PDMP strategies, Zig-Zag and the Bouncy Particle Sampler (BPS) can be readily analysed with some surprising conclusions; not least that the BPS has some undesirable asymptotic reducibility properties as dimension diverges.
Most of the results in this area assume stationarity, but work on the transient phase will also be at least briefly described.
https://circle.library.ubc.ca/rest/handle/2429/70146?expand=metadata
117.0
video/mp4
Author affiliation: University of Warwick
Oaxaca (Mexico : State)
10.14288/1.0378702
eng
Unreviewed
Vancouver : University of British Columbia Library
Banff International Research Station for Mathematical Innovation and Discovery
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Faculty
BIRS Workshop Lecture Videos (Oaxaca (Mexico : State))
Mathematics
Statistics
Statistical mechanics, structure of matter
Statistical mechanics
(Hands-on + discussion) Scaling limits for modern MCMC algorithms
Moving Image
http://hdl.handle.net/2429/70146