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A Literature Review of Stochastic Modeling for Phylogenetic Comparative Analysis in Trait Evolution

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  • Dwueng-Chwuan Jhwueng

    (Department of Statistics, Feng-Chia University, Taichung 40724, Taiwan)

Abstract

Evolutionary inferences from phylogenetic trees can be modeled stochastically using a range of mathematical frameworks. Among these, stochastic differential equations (SDEs) provide a particularly flexible and powerful approach to capturing the continuous-time dynamics of evolutionary processes. This review summarizes advances in stochastic modeling for trait evolution along a phylogenetic tree, with a focus on stochastic differential equations (SDEs), Gaussian and non-Gaussian processes, and time series models that can be expressed as special cases of general stochastic frameworks, depending on the questions being addressed or the types of data analyzed. We explore current developments and future research directions of stochastic modeling for phylogenetic comparative analysis in trait evolution.

Suggested Citation

  • Dwueng-Chwuan Jhwueng, 2025. "A Literature Review of Stochastic Modeling for Phylogenetic Comparative Analysis in Trait Evolution," Mathematics, MDPI, vol. 13(3), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:361-:d:1574517
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    References listed on IDEAS

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    1. Dwueng-Chwuan Jhwueng & Chia-Hua Chang, 2025. "Stochastic Modeling of Adaptive Trait Evolution in Phylogenetics: A Polynomial Regression and Approximate Bayesian Computation Approach," Mathematics, MDPI, vol. 13(1), pages 1-21, January.
    2. Mark Pagel, 1999. "Inferring the historical patterns of biological evolution," Nature, Nature, vol. 401(6756), pages 877-884, October.
    3. Jhwueng, Dwueng-Chwuan, 2020. "Modeling rate of adaptive trait evolution using Cox–Ingersoll–Ross process: An Approximate Bayesian Computation approach," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    4. Ho, Lam Si Tung & Dinh, Vu, 2022. "When can we reconstruct the ancestral state? A unified theory," Theoretical Population Biology, Elsevier, vol. 148(C), pages 22-27.
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