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Approximate Bayesian Computation for Discrete Spaces
2021
Entropy
Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by
doi:10.3390/e23030312
pmid:33800743
pmcid:PMC7998962
fatcat:ilvcnbdmazevtlrlqcdim4maai