Bayesian Inference in Phylogeny via Sequential Stochastic Approximation Monte Carlo

• 발행기관: 한국자료분석학회
• 저자명: Sooyoung Cheon Hyo Sung Kim
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.11 No.3, 1221~1231쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2009.06.30

영문 초록

The Sequential Stochastic approximation Monte Carlo(SSAMC) algorithm has recently been proposed by Cheon and Liang(2008) as a new phylogenetic tree construction method. SSAMC is an efficient algorithm to alleviate local trap problems and the curse of dimensionality in simulations simultaneously by making use of the sequential structure of phylogenetic trees in conjunction with stochastic approximation Monte Carlo(SAMC) simulations. In this paper, we discuss the application of SSAMC to the Bayesian inference in phylogeny. Two real datasets are used for SSAMC to show the capability of a phylogeny tree reconstruction and existing Bayesian methods, BAMBE and MrBayes, are applied for comparison. Numerical results indicate that SSAMC is a useful algorithm for phylogeny inference in terms of quality of the resulting phylogenetic trees.

목차

1. Introduction

2. Bayesian Approach in Phylogenetic Trees

3. Sequential Stochastic approximation Monte Carlo

4. Numerical results

5. Conclusion

Reference

키워드

Bayesian inference Phylogeny tree construction Curse of dimensionality Local trap Markov chain Monte Carlo Sequential Stochastic approximation Monte Carlo