approximate bayesian computation
Approximate Bayesian Computation (ABC) is a computational method used in statistics and machine learning to approximate the posterior distribution of model parameters by simulating data from the model and comparing it to observed data, allowing for parameter inference without having to compute the likelihood explicitly.
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