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通过数值代数几何对对称群模型进行极大似然估计。

Maximum Likelihood Estimation of Symmetric Group-Based Models via Numerical Algebraic Geometry.

机构信息

School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.

Department of Mathematics and Systems Analysis, Aalto University, Espoo, Finland.

出版信息

Bull Math Biol. 2019 Feb;81(2):337-360. doi: 10.1007/s11538-018-0523-2. Epub 2018 Oct 24.

Abstract

Phylogenetic models admit polynomial parametrization maps in terms of the root distribution and transition probabilities along the edges of the phylogenetic tree. For symmetric continuous-time group-based models, Matsen studied the polynomial inequalities that characterize the joint probabilities in the image of these parametrizations (Matsen in IEEE/ACM Trans Comput Biol Bioinform 6:89-95, 2009). We employ this description for maximum likelihood estimation via numerical algebraic geometry. In particular, we explore an example where the maximum likelihood estimate does not exist, which would be difficult to discover without using algebraic methods.

摘要

系统发育模型允许根据根分布和系统发育树边缘的转移概率进行多项式参数化映射。对于对称连续时间基于组的模型,Matsen 研究了刻画这些参数化图像中联合概率的多项式不等式(Matsen 在 IEEE/ACM Trans Comput Biol Bioinform 6:89-95, 2009)。我们通过数值代数几何来进行最大似然估计。特别是,我们探索了一个最大似然估计不存在的例子,如果不使用代数方法,很难发现这个例子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebc9/6342846/906c72c0d830/11538_2018_523_Fig1_HTML.jpg

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