TreeFlow: probabilistic programming and automatic differentiation for phylogenetics
Probabilistic programming frameworks are powerful tools for statistical modelling and inference. They are not immediately generalisable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic programming and automatic differentiation with phylogenetic trees. It implements inference algorithms for phylogenetic tree times and model parameters given a tree topology. We demonstrate how TreeFlow can be used to quickly implement and assess new models. We also show that it provides reasonable performance for gradient-based inference algorithms compared to specialized computational libraries for phylogenetics.
Code (1)
Tasks
Probabilistic ProgrammingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
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. These calculations are readily available for arbitrary models via auto…
Variational InferenceReversible Jump Probabilistic Programming
In this paper we present a method for automatically deriving a Reversible Jump Markov chain Monte Carlo sampler from probabilistic programs that specify the target and proposal distributions. The main challenge in automa…
Bayesian InferenceCode GenerationProbabilistic ProgrammingImproved Marginal Unbiased Score Expansion (MUSE) via Implicit Differentiation
We apply the technique of implicit differentiation to boost performance, reduce numerical error, and remove required user-tuning in the Marginal Unbiased Score Expansion (MUSE) algorithm for hierarchical Bayesian inferen…
Bayesian InferenceProbabilistic ProgrammingProbabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle filter (APF) with automatic Rao-Blackwelli…
Probabilistic ProgrammingAutomatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this proces…
Probabilistic ProgrammingVariational Inference