No-U-Turn sampling for phylogenetic trees

Colloquium by Johannes Wahle

Time: Tuesday, 19th January 2021, 1pm sharp




Meeting-ID: 962 1066 2701

Password: 551011


Speaker: Johannes Wahle, who is a Member of the ERC Project CrossLingference with Prof. Dr. Gerhard Jäger and former member of the center


Title:No-U-Turn sampling for phylogenetic trees



Phylogenetic trees are important for our understanding of evolutionary relationships. But even for only a small number of entities the number of possible trees is immense. In order to efficiently search through this large space and analyze the distributions of these trees, different algorithmic solutions have been proposed. Bayesian inference of such trees is predominantly based on the Metropolis-Hastings algorithm. For high dimensional and correlated data this algorithm is known to be inefficient. There are gradient based algorithms to speed up such inference. Building on recent research which uses gradient based approaches for the inference of phylogenetic trees in a Bayesian framework, I present an algorithm which is capable of performing No-U-Turn sampling for phylogenetic trees. As an extension to Hamiltonian Monte Carlo methods, No-U-Turn sampling comes with the same benefits, such as proposing distant new states with a high acceptance probability, but eliminates the need to manually tune hyper parameters. Evaluated on three different data sets, the new sampler shows that it converges faster to the target distribution. The results also indicate that a higher number of topologies are traversed during sampling by the new algorithm in comparison to traditional Markov Chain Monte Carlo approaches. This new algorithm leads to a more efficient exploration of the posterior distribution of phylogenetic tree topologies.