Approximate Bayesian computation for Markovian binary trees in phylogenetics

2023-09-01 · Mingqi He, Sophie Hautphenne, Yao-ban Chan

Phylogenetic trees describe the relationships between species in the evolutionary process, and provide information about the rates of diversification. To understand the mechanisms behind macroevolution, we consider a class of multitype branching processes called Markovian binary trees (MBTs). MBTs allow for trait-based variation in diversification rates, and provide a flexible and realistic probabilistic model for phylogenetic trees. We develop an approximate Bayesian computation (ABC) scheme to infer the rates of MBT parameters by exploiting the information in the shapes of phylogenetic trees. We evaluate the accuracy of this inference method using simulation studies, and find that our method is able to detect variation in the diversification rates, with accuracy comparable to, and generally better than, likelihood-based methods. In an application to a real-life phylogeny of squamata, we reinforce conclusions drawn from earlier studies, in particular supporting the existence of ovi-/viviparity transitions in both directions. Our method demonstrates the potential for more complex models of evolution to be employed in phylogenetic inference, in conjunction with likelihood-free schemes.

📄 PDF Abstract BibTeX arXiv:2309.00194

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Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards

2024-06-20 · Noah Topper, Alvaro Velasquez, George Atia

Inverse reinforcement learning (IRL) is the problem of inferring a reward function from expert behavior. There are several approaches to IRL, but most are designed to learn a Markovian reward. However, a reward function …

BIRL reinforcement-learning Reinforcement Learning

An Infinite BART model

2025-11-25 · Marco Battiston, Yu Luo arxiv

Bayesian additive regression trees (BART) are popular Bayesian ensemble models used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of deci…

Sparse Algorithms for Markovian Gaussian Processes

2021-03-19 · William J. Wilkinson, Arno Solin, Vincent Adam

Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series. Sparse Markovian Gaussian processes combine the use of inducing variable…

Bayesian Inference Gaussian Processes Time Series Time Series Analysis+1

On the Tractability of SHAP Explanations under Markovian Distributions

2024-05-05 · Reda Marzouk, Colin de la Higuera

Thanks to its solid theoretical foundation, the SHAP framework is arguably one the most widely utilized frameworks for local explainability of ML models. Despite its popularity, its exact computation is known to be very …

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

2019-01-29 · Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen

We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characteri…

Time Series Time Series Analysis