Approximate Bayesian Computation for Uncertainty-Free Inference Problems

ABC-Di: Approximate Bayesian Computation for Discrete Data

We propose to use a population-based population-based population-based approximate bayesian computation (mcmc) framework to solve likelihood-free inference problems.Further, we present a valid markov kernel, and propose a new kernel that is inspired by differential evolution.We assess the proposed approach on a problem with the known likelihood function, namely, discovering the underlying diseases based on a quantum monte carlo (qmr)-differential evolution (ded) network, and three likelihood-free inference problems : (i) the qmr-differential evolution (ded) network with the unknown likelihood function, (ii)learning binary neural network, and (iii)neural architecture search.The obtained results indicate the high potential of the proposed framework and the superiority of the new kernel.