Reflections on the bouncy particle sampler and Zig-Zag sampler


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

講 師:
Joris Bierkens (Delft Institute of Applied Mathematics, TU Delft)
テーマ:
Reflections on the bouncy particle sampler and Zig-Zag sampler
日 時:
2018年10月02日(火)14:40-16:10
場 所:
大阪大学豊中キャンパス基礎工学研究科I棟204号室
参加費:
無料
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