论文标题
贝叶斯系统发育推断的粒子吉布斯采样
Particle Gibbs Sampling for Bayesian Phylogenetic inference
论文作者
论文摘要
联合序列蒙特卡洛（CSMC）已被证明是使用生物学序列的标准马尔可夫链蒙特卡洛（MCMC）的有效互补方法。将CSMC和MCMC组合在粒子吉布斯（PG）采样器的框架中以共同估计系统发育树和进化参数，这很有吸引力。但是，如果底层SMC遭受路径变性问题，则粒子吉布斯的马尔可夫链可能会糟糕。已经提出了一些补救措施，包括带有祖先采样和相互作用的粒子MCMC的粒子吉布斯，以改善PG。但是它们要么不能应用于组合树空间中，要么保持效率低下。我们通过提出更有效的建议分布来引入一种新型的CSMC方法。它也可以将其合并到粒子Gibbs采样器框架中，以推断进化模型中的参数。通过在不同的计算核心上分配样品，可以轻松地平行新算法。我们验证开发的CSMC可以通过数值实验在各种粒子Gibbs采样器中更有效地采样树木。我们的实施可从https://github.com/liangliangwangsfu/phylopmcmc获得
The combinatorial sequential Monte Carlo (CSMC) has been demonstrated to be an efficient complementary method to the standard Markov chain Monte Carlo (MCMC) for Bayesian phylogenetic tree inference using biological sequences. It is appealing to combine the CSMC and MCMC in the framework of the particle Gibbs (PG) sampler to jointly estimate the phylogenetic trees and evolutionary parameters. However, the Markov chain of the particle Gibbs may mix poorly if the underlying SMC suffers from the path degeneracy issue. Some remedies, including the particle Gibbs with ancestor sampling and the interacting particle MCMC, have been proposed to improve the PG. But they either cannot be applied to or remain inefficient for the combinatorial tree space. We introduce a novel CSMC method by proposing a more efficient proposal distribution. It also can be combined into the particle Gibbs sampler framework to infer parameters in the evolutionary model. The new algorithm can be easily parallelized by allocating samples over different computing cores. We validate that the developed CSMC can sample trees more efficiently in various particle Gibbs samplers via numerical experiments. Our implementation is available at https://github.com/liangliangwangsfu/phyloPMCMC
