Our new paper introduces a novel noise-insensitive and efficient parameter inference algorithm

We introduced ABC-SMC-DRF, a novel likelihood-free parameter inference method that is both insensitive to noise and efficient with regards to computational runtime and storage, due to the implementation of random forests and sequential Monte Carlo. Read a related press release from IICD about the challenges in parameter estimation that ABC-SMC-DRF is designed to solve, and how it can be applied in different scientific areas. ABC-SMC-DRF can be installed from GitHub here.