Bayesian phylogenetics, which approximates a posterior distribution of phylogenetic trees, has become more and more popular with the development of Monte Carlo methods. Standard Bayesian estimation of phylogenetic trees can handle rich evolutionary models but requires expensive Markov chain Monte Carlo (MCMC) simulations, which may suffer from two difficulties, the curse of dimensionality and the local-trap problem. Our previous work [1] has shown that the combinatorial sequential Monte Carlo (CSMC) method can serve as a good alternative to MCMC in posterior inference over phylogenetic trees. However, the simple proposal distribution used in CSMC is inefficient to combine with MCMC in the framework of the particle Gibbs sampler. Moreover, CSMC is inapplicable to the particle Gibbs with ancestor sampling [2]. In this talk, we will present a more efficient CSMC method, called CSMC-BF, with a more flexible proposal. The proposed CSMC-BF can improve the performance of the particle Gibbs sampler compared with the original CSMC, and can be used in the particle Gibbs with ancestor sampling. We will demonstrate the advantages of the proposed CSMC-BF using simulation studies and real data analysis. References [1] Liangliang Wang, Alexandre Bouchard-Côté, and Arnaud Doucet. Bayesian phylogenetic inference using a combinatorial sequential Monte Carlo method. Journal of the American Statistical Association, 110(512):13621374, 2015. [2] Fredrik Lindsten, Michael I. Jordan, and Thomas B. Schön. Particle Gibbs with ancestor sampling. Journal of Machine Learning Research, 15(1):21452184, 2014.urvival models.