• 论文 •

### 基于序贯性蒙特卡洛采样器的并行采样方法

1. 1. SJTU, 上海交通大学数学科学学院, 上海 200240;
2. UoB, 伯明翰大学 数学学院, 英国 B15 2TT
• 收稿日期:2021-04-10 发布日期:2022-09-09
• 基金资助:
国家自然科学基金（11771289）资助.

Wu Jiangqi, Li Jinglai. A PARALLEL SAMPLING METHOD BASED ON SEQUENTIAL MONTE CARLO SAMPLER[J]. Journal on Numerica Methods and Computer Applications, 2022, 43(3): 281-294.

### A PARALLEL SAMPLING METHOD BASED ON SEQUENTIAL MONTE CARLO SAMPLER

Wu Jiangqi1, Li Jinglai2

1. 1. SJTU, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China;
2. UoB, School of Mathematics, University of Birmingham, Birmingham B15 2TT, UK
• Received:2021-04-10 Published:2022-09-09

Many scientific and engineering applications require to draw samples from a given distribution. A typical example is Bayesian inference where the one needs to draw samples from the posterior distribution. The often used Markov chain Monte Carlo methods are not suitable in the parallel computing situation. In this work we propose to use the sequential Monte Carlo sampler to solve such problems. In particular we propose two forward proposal distributions:a Gaussian random walk proposal and a Langevin proposal, and we also derive the corresponding backward proposals. With numerical examples, we demonstrate that the sequential Monte Carlo sampler has a better performance than the multi-chain Markov chain Monte Carlo methods.

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