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Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 47

Automatic perturbation kernels for biological applications of Approximate Bayesian Computation sequential Monte Carlo with random forests

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DOI: http://dx.doi.org/10.15439/2026F2839

Citation: Yanjie Chen,

Full text

Abstract. We recently introduced Approximate Bayesian Computation sequential Monte Carlo with random forests (ABC-SMC-RF), a novel likelihood-free inference method that combines the robustness of random forests and the efficiency of the Approximate Bayesian Computation sequential Monte Carlo (ABC-SMC) framework. In this work, we examine the selection of parameter perturbation kernels and their impact on ABC-SMC-RF's results. We evaluate the performance of an automatic and adaptive perturbation scheme, previously developed for ABC-SMC. Across examples drawn from different realistic biological contexts, we show that (i) ABC-SMC-RF produces precise and accurate parameter inferences, without requiring extensive hyper-parameter tuning, and (ii) the adaptive perturbations yield optimal performance in ABC-SMC-RF, further reducing practical barriers to its adoption in many settings. We expect ABC-SMC-RF, armed with the automatic perturbation scheme, to be well positioned for supporting different scientific problems that require robust, efficient and convenient parameter estimation.

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