Stereographic Barker’s MCMC Proposal: Efficiency and Robustness at Your Disposal

Abstract: We introduce a new family of robust gradient-based MCMC samplers under the framework of stereographic MCMC (Yang et al. 2022) which maps the original high dimensional problem in Euclidean space onto a sphere. Compared with the existing Stereographic Projection Sampler (SPS) which is of a random-walk Metropolis type algorithm, our new family of samplers is gradient-based using the Barker proposal (Livingstone and Zanella, 2022), which improves SPS in high dimensions and is robust to tuning. Meanwhile, the proposed algorithms enjoy all the good properties of SPS, such as uniform ergodicity for a large class of heavy and light-tailed distributions and "blessings of dimensionality".

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.