A probabilistic approach to the statistical analysis of phylogenetics
Statistical analysis of phylogenetic models is currently one of the most active research areas in computational biology, with wide applications in the theory of evolution, epidemiology and forensics. Existing computational approaches to inference based on Markov Chain Monte Carlo (MCMC) methods are not so efficient as Sequential Monte Carlo (SMC) inference algorithms. The goal of the EU-funded PhyPPL project is to apply probabilistic programming to automatically generate SMC inference models for those phylogenetic problems difficult to solve with MCMC methods. Specifically, the project will design statistical inference algorithms for complex diversification models with variable tree topology and a trait-dependent branching process. To demonstrate the potential of the new algorithms, the project will use them to trace the impact of the Andean orogeny on neotropical biodiversity.
Call for proposal
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