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Found 6 papers, 6 papers with code
Torchtree: flexible phylogenetic model development and inference using PyTorch
Furthermore, we explore the use of the forward KL divergence as an optimizing criterion for variational inference, which can handle discontinuous and non-differentiable models.
Differentiable Phylogenetics via Hyperbolic Embeddings with Dodonaphy
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
Probabilistic programming frameworks are powerful tools for statistical modelling and inference.
Automatic differentiation is no panacea for phylogenetic gradient computation
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning.
Fidelity of Hyperbolic Space for Bayesian Phylogenetic Inference
In this paper, we embed genomic sequences into hyperbolic space and perform hyperbolic Markov Chain Monte Carlo for Bayesian inference.
19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology
The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model.
