Differentiable Phylogenetics via Hyperbolic Embeddings with Dodonaphy

1 code implementation • 21 Sep 2023 • Matthew Macaulay, Mathieu Fourment

We present soft-NJ, a differentiable version of neighbour-joining that enables gradient-based optimisation over the space of trees.

TreeFlow: probabilistic programming and automatic differentiation for phylogenetics

1 code implementation • 9 Nov 2022 • Christiaan Swanepoel, Mathieu Fourment, Xiang Ji, Hassan Nasif, Marc A Suchard, Frederick A Matsen IV, Alexei Drummond

Probabilistic programming frameworks are powerful tools for statistical modelling and inference.

Probabilistic Programming

Automatic differentiation is no panacea for phylogenetic gradient computation

2 code implementations • 3 Nov 2022 • Mathieu Fourment, Christiaan J. Swanepoel, Jared G. Galloway, Xiang Ji, Karthik Gangavarapu, Marc A. Suchard, Frederick A. Matsen IV

Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning.

Variational Inference

Fidelity of Hyperbolic Space for Bayesian Phylogenetic Inference

1 code implementation • 16 Jun 2022 • Matthew Macaulay, Aaron E. Darling, Mathieu Fourment

In this paper, we embed genomic sequences into hyperbolic space and perform hyperbolic Markov Chain Monte Carlo for Bayesian inference.

Bayesian Inference Navigate

19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology

1 code implementation • 28 Nov 2018 • Mathieu Fourment, Andrew F. Magee, Chris Whidden, Arman Bilge, Frederick A. Matsen IV, Vladimir N. Minin

The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model.