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Business Analytics Seminars

The seminars are on Fridays at 11am in Room 498, Merewether Building (cnr of City Road and Butlin Avenue), unless otherwise specified.

The seminar organiser is Laurent Pauwels.

9th Jan 2015 - 11:00 am

Speaker:

Associate Professor Martin Burda,

Affiliation:

University of Toronto

Venue:

Seminar Room 498, Merewether Building H04

Title:

Bayesian Adaptively Updated Hamiltonian Monte Carlo

Abstract:

Hamiltonian Monte Carlo (HMC) is a recent statistical procedure to sample from complex distributions. Distant proposal draws are taken in a sequence of steps following the Hamiltonian dynamics of the underlying parameter space, often yielding superior mixing properties of the resulting Markov chain. However, its performance can deteriorate sharply with the degree of irregularity of the underlying likelihood due to its lack of local adaptability in the parameter space. Riemann Manifold HMC (RMHMC), a locally adaptive version of HMC, alleviates this problem, but at a substantially increased computational cost that can become prohibitive in high-dimensional scenarios. In this paper we propose the Adaptively Updated HMC (AUHMC), an alternative inferential method based on HMC that is both fast and locally adaptive, combining the advantages of both HMC and RMHMC. The benefits become more pronounced with higher dimensionality of the parameter space and with the degree of irregularity of the underlying likelihood surface. We show that AUHMC satisfies detailed balance for a valid MCMC scheme and provide a comparison with RMHMC in terms of effective sample size, highlighting substantial efficiency gains of AUHMC. Simulation examples and an application of the BEKK GARCH model show the practical usefulness of the new posterior sampler.