Approximate Bayesian Computation with Domain Expert in the Loop. (arXiv:2201.12090v1 [stat.ML])

Web: http://arxiv.org/abs/2201.12090

Approximate Bayesian computation (ABC) is a popular likelihood-free inference
method for models with intractable likelihood functions. As ABC methods usually
rely on comparing summary statistics of observed and simulated data, the choice
of the statistics is crucial. This choice involves a trade-off between loss of
information and dimensionality reduction, and is often determined based on
domain knowledge. However, handcrafting and selecting suitable statistics is a
laborious task involving multiple trial-and-error steps. In this work, we
introduce an active learning method …

arxiv bayesian ml