Parallel Tempering on Optimized Paths. Joint work with Vittorio Romaniello, Saifuddin Syed, and Alexandre Bouchard-Cote at UBC
Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms that constructs a path of distributions annealing between a tractable reference and an intractable target, and then interchanges states along the path to improve mixing in the target. The performance of PT depends on how quickly a sample from the reference distribution makes its way to the target, which in turn depends on the particular path of annealing distributions. However, past work on PT has used only simple paths constructed from convex combinations of the reference and target log-densities. In this talk we will show that this path performs poorly in the common setting where the reference and target are nearly mutually singular. To address this issue, we will present an extension of the PT framework to general families of paths, formulate the choice of path as an optimization problem that admits tractable gradient estimates, and present a flexible new family of spline interpolation paths for use in practice. Theoretical and empirical results will demonstrate that the proposed methodology breaks previously-established upper performance limits for traditional paths.
Presenter: Trevor Campbell is an assistant professor of statistics at the University of British Columbia. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and Bayesian theory. He was previously a postdoctoral associate advised by Tamara Broderick
in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT.
The event is free but you do need to register.
@StatSocAus
