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Activity Number:
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250
- Topics in Statistical Learning
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #328310
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Title:
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Structure Learning for Phylogenetic Tree with Quantitative Characters
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Author(s):
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Chaoyu Yu* and Mathias Drton
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Companies:
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and University of Washington
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Keywords:
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Tree;
Structure Learning;
Phylogenetics;
High-dimensional
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Abstract:
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We consider the problem of learning the tree topology in phylogenetic analysis of continuous data. The models we treat assume that the evaluation of the continuous characters along the phylogenetic tree follows a Brownian motion process. We propose and compare three methods for recovery of the tree structure. The first is based on independence tests, and the second is based on the size of sample covariances. As a third option, we derive a structural EM algorithm for searching the tree structure and corresponding parameter estimates that maximize the likelihood of the observed data. We discuss asymptotic consistency of our methods and examine the sample size needed for tree recovery under different scenarios.
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Authors who are presenting talks have a * after their name.
