University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Adaptive Hamiltonian-based MCMC samplers

Adaptive Hamiltonian-based MCMC samplers

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Colorado Reed.

In this RCC we discuss the widely-experienced difficulty in tuning Monte Carlo samplers based on simulating Hamiltonian dynamics. We develop an algorithm that allows for the adaptation of Hamiltonian and Riemann manifold Hamiltonian Monte Carlo samplers using Bayesian optimization that allows for infinite adaptation of the parameters of these samplers. We show that the resulting samplers are ergodic, and that the use of our adaptive algorithms makes it easy to obtain more efficient samplers, in some cases precluding the need for more complex solutions. Hamiltonian-based Monte Carlo samplers are widely known to be an excellent choice of MCMC method, and such approaches remove a key obstacle towards the more widespread use of these samplers in practice.

This talk is part of the Machine Learning Reading Group @ CUED series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity