Introduces distributional random forests for joint posterior inference and an SMC update for the prior in ABC, claiming accurate posteriors across deterministic and stochastic models.
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Approximate Bayesian Computation sequential Monte Carlo via random forests
Introduces distributional random forests for joint posterior inference and an SMC update for the prior in ABC, claiming accurate posteriors across deterministic and stochastic models.