In Bayesian phylogenetics, the goal is to approximate a posterior distribution of phylogenetic trees based on biological data. Standard Bayesian estimation of phylogenetic trees can handle rich evolutionary models but requires expensive Markov chain Monte Carlo (MCMC) simulations, which may suffer from the curse of dimensionality and the local-trap problem. Previous work has shown that the sequential Monte Carlo (SMC) method can serve as an excellent alternative to MCMC in posterior inference. In this talk, I will talk about our SMC methods for Bayesian Phylogenetic Inference and illustrate them using simulation studies and real data analysis.