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Updated: Apr 19, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A simulation approach for change-points on phylogenetic trees.

Adam Persing¹, Ajay Jasra, Alexandros Beskos

¹1 Department of Statistical Science, University College London , London, United Kingdom .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 16, 2014

Summary

This summary is machine-generated.

This study introduces a novel time-machine particle marginal Metropolis-Hastings (PMMH) algorithm for Bayesian inference in evolutionary biology. The method efficiently handles complex models with unknown evolutionary parameters and change-points, outperforming existing techniques.

Keywords:

approximate Bayesian computation binary trees change-point models particle marginal Metropolis-Hastings sequential Monte Carlo samplers time machine

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Area of Science:

Computational evolutionary biology
Statistical phylogenetics
Bayesian inference

Background:

Phylogenetic inference requires understanding sequence evolution across sites.
Evolutionary models often have parameters that vary over sites and change over time.
Bayesian inference for these complex models presents significant computational challenges.

Purpose of the Study:

To develop a computationally efficient Bayesian inference method for evolutionary models with site-specific, time-varying parameters.
To address the transdimensional nature of the posterior distribution in such models.
To improve upon existing approximate Bayesian computation (ABC) techniques.

Main Methods:

Developed a 'time machine' approximation to reduce computational cost by approximating ancestral states.
Implemented a particle marginal Metropolis-Hastings (PMMH) algorithm incorporating sequential Monte Carlo (SMC) sampling.
Combined the time machine approximation with PMMH for efficient transdimensional inference.

Main Results:

The time machine PMMH algorithm significantly reduces computational cost, scaling linearly with the number of sequences.
The method effectively handles models with an unknown number of parameter change-points.
Empirical evaluations on simulated and real data demonstrate superior performance compared to ABC methods.

Conclusions:

The time machine PMMH algorithm offers a powerful and efficient solution for Bayesian inference in complex evolutionary models.
This approach mitigates key computational bottlenecks, enabling more robust phylogenetic analyses.
The method shows significant potential for advancing evolutionary sequence analysis and related fields.