A random-forest surrogate for likelihood change under tree moves enables delayed-acceptance SMC that cuts expensive likelihood evaluations while preserving posterior estimates on simulated and real phylogenetic data.
Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters
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Accelerating Bayesian Phylogenetic Inference via Delayed Acceptance Sequential Monte Carlo with Random Forest Surrogates
A random-forest surrogate for likelihood change under tree moves enables delayed-acceptance SMC that cuts expensive likelihood evaluations while preserving posterior estimates on simulated and real phylogenetic data.