What's on in Computing Science?
Date: Thursday, 4 February, 2010
Sir Alwyn Williams Building, 422 Seminar Room
[ Inference Seminar ] Hybrid Monte-Carlo in high dimensions
Alex Beskos (University College London)
We have investigated algorithmic properties of Hybrid Monte-Carlo (HMC)
in high dimensions.
In the simplified scenario of iid target distributions we
have found that the asymptotically optimal acceptance probability, for
dimensionality growing to infinity,
is 0.651 (to three decimal places), irrespectively of the particular
We have also worked on a semi-implicit version of the leapfrog
integrator, which is relevant for target distributions
defined as a change of measure from Gaussian laws arising in
applications. We illustrate that implementation
of the semi-implicit version of the integrator in such a context allows
for the construction of a well-defined HMC algorithm
in infinite-dimensional Hilbert spaces.
Contact: Dr Rónán Daly (email@example.com)
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