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torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch.

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Title: torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch.
Authors: Fourment, Mathieu1 (AUTHOR), Macaulay, Matthew1 (AUTHOR), Swanepoel, Christiaan J2 (AUTHOR), Ji, Xiang3 (AUTHOR), Suchard, Marc A4 (AUTHOR), Matsen IV, Frederick A5 (AUTHOR)
Source: Systematic Biology. Jan2026, Vol. 75 Issue 1, p39-51. 13p.
Subjects: Phylogenetic models, Variational approach (Mathematics), Bayesian analysis, Statistical models, Automatic differentiation, Markov chain Monte Carlo
Abstract: Bayesian inference has predominantly relied on the Markov chain Monte Carlo (MCMC) algorithm for many years. However, MCMC is computationally laborious, especially for complex phylogenetic models of time trees. This bottleneck has led to the search for alternatives, such as variational Bayes, which can scale better to large data sets. In this paper, we introduce torchtree , a framework written in Python that allows developers to easily implement rich phylogenetic models and algorithms using a fixed tree topology. One can either use automatic differentiation or leverage torchtree 's plug-in system to compute gradients analytically for model components for which automatic differentiation is slow. We demonstrate that the torchtree variational inference framework performs similarly to BEAST in terms of speed, and delivers promising approximation results, though accuracy varies across scenarios. Furthermore, we explore the use of the forward Kullback–Leibler (KL) divergence as an optimizing criterion for variational inference, which can handle discontinuous and nondifferentiable models. Our experiments show that inference using the forward KL divergence is frequently faster per iteration compared with the evidence lower bound (ELBO) criterion, although the ELBO-based inference may converge faster in some cases. Overall, torchtree provides a flexible and efficient framework for phylogenetic model development and inference using PyTorch. [ABSTRACT FROM AUTHOR]
Copyright of Systematic Biology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 190916331
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch.
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  Data: <searchLink fieldCode="JN" term="%22Systematic+Biology%22">Systematic Biology</searchLink>. Jan2026, Vol. 75 Issue 1, p39-51. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Phylogenetic+models%22">Phylogenetic models</searchLink><br /><searchLink fieldCode="DE" term="%22Variational+approach+%28Mathematics%29%22">Variational approach (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+differentiation%22">Automatic differentiation</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink>
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  Data: Bayesian inference has predominantly relied on the Markov chain Monte Carlo (MCMC) algorithm for many years. However, MCMC is computationally laborious, especially for complex phylogenetic models of time trees. This bottleneck has led to the search for alternatives, such as variational Bayes, which can scale better to large data sets. In this paper, we introduce torchtree , a framework written in Python that allows developers to easily implement rich phylogenetic models and algorithms using a fixed tree topology. One can either use automatic differentiation or leverage torchtree 's plug-in system to compute gradients analytically for model components for which automatic differentiation is slow. We demonstrate that the torchtree variational inference framework performs similarly to BEAST in terms of speed, and delivers promising approximation results, though accuracy varies across scenarios. Furthermore, we explore the use of the forward Kullback–Leibler (KL) divergence as an optimizing criterion for variational inference, which can handle discontinuous and nondifferentiable models. Our experiments show that inference using the forward KL divergence is frequently faster per iteration compared with the evidence lower bound (ELBO) criterion, although the ELBO-based inference may converge faster in some cases. Overall, torchtree provides a flexible and efficient framework for phylogenetic model development and inference using PyTorch. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Systematic Biology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1093/sysbio/syaf047
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 39
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      – SubjectFull: Phylogenetic models
        Type: general
      – SubjectFull: Variational approach (Mathematics)
        Type: general
      – SubjectFull: Bayesian analysis
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      – SubjectFull: Statistical models
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      – SubjectFull: Automatic differentiation
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      – SubjectFull: Markov chain Monte Carlo
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      – TitleFull: torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch.
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              Text: Jan2026
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              Y: 2026
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