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An overview on Approximate Bayesian Computation

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If you have a question about this talk, please contact Mo Dick Wong.

Approximate Bayesian Computation(ABC) is a likelihood-free technique to compute/sample the posterior distribution of parameters from observed data. It has become popular in recent years due to its adaptability and flexibility. In this talk, I shall explain how ABC works with various sampling methods and provide some convergence results on cost-error trade off.

This talk is part of the Cambridge Analysts' Knowledge Exchange series.

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