大阪大学　数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,Osaka University

Reflections on the bouncy particle sampler and Zig-Zag sampler

Joris Bierkens (Delft Institute of Applied Mathematics, TU Delft)

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4. Reflections on the bouncy particle sampler and Zig-Zag sampler

大阪大学 数理・データ科学セミナー データ科学セミナーシリーズ 第36回

Reflections on the bouncy particle sampler and Zig-Zag sampler

Joris Bierkens (Delft Institute of Applied Mathematics, TU Delft)

In recent years piecewise deterministic Markov processes (PDMPs) have emerged as a promising alternative to classical MCMC algorithms. In particular these PDMP based algorithms have good convergence properties and allow for efficient subsampling. Although many different PDMP based algorithms can be designed, two algorithms play fundamental roles: the Bouncy Particle sampler and the Zig-Zag sampler. In this talk both algorithms will be introduced and a comparison of properties of these algorithms will be presented, including recent results on ergodicity and on scaling with respect to dimension.

This is a joint work with Pierre-André Zitt, Kengo Kamatani and Gareth Roberts.