|
Activity Number:
|
234
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Monday, August 1, 2016 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
International Statistical Institute
|
|
Abstract #320407
|
View Presentation
|
|
Title:
|
Brownian Motion Tree Models: Theory and Applications
|
|
Author(s):
|
Caroline Uhler*
|
|
Companies:
|
MIT
|
|
Keywords:
|
Brownian motion model ;
non-convex optimization ;
covariance estimation ;
phylogenetics
|
|
Abstract:
|
Brownian motion tree models are heavily used for phylogenetic analysis based on continuous characters and as network tomography models to analyze the connections in the Internet. These models are a special instance of Gaussian models with linear constraints on the covariance matrix. Maximum likelihood estimation in this model class leads to a non-convex optimization problem that typically has many local maxima. Current methods for tree and parameter estimation are based on heuristics with no guarantees. I will present efficient algorithms and explain how to initiate the algorithms in a data-informed way to obtain provable guarantees for learning the tree topology and the branch lengths.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2016 program
|
