Improving Tree Probability Estimation with Stochastic Optimization and Variance Reduction
Probability estimation of tree topologies is one of the fundamental tasks in phylogenetic inference. The recently proposed subsplit Bayesian networks (SBNs) provide a powerful probabilistic graphical model for tree topology probability estimation by properly leveraging the hierarchical structure of...
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Zusammenfassung: | Probability estimation of tree topologies is one of the fundamental tasks in
phylogenetic inference. The recently proposed subsplit Bayesian networks (SBNs)
provide a powerful probabilistic graphical model for tree topology probability
estimation by properly leveraging the hierarchical structure of phylogenetic
trees. However, the expectation maximization (EM) method currently used for
learning SBN parameters does not scale up to large data sets. In this paper, we
introduce several computationally efficient methods for training SBNs and show
that variance reduction could be the key for better performance. Furthermore,
we also introduce the variance reduction technique to improve the optimization
of SBN parameters for variational Bayesian phylogenetic inference (VBPI).
Extensive synthetic and real data experiments demonstrate that our methods
outperform previous baseline methods on the tasks of tree topology probability
estimation as well as Bayesian phylogenetic inference using SBNs. |
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DOI: | 10.48550/arxiv.2409.05282 |