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Montpellier Math Colloquium
Approximative Bayesian Computation (ABC) methods allow approximating intractable likelihoods in Bayesian inference. ABC rejection sampling simulates parameters from the prior and keeps those where simulated data is close to observed data. ABC Markov chain Monte Carlo creates a Markov chain over the parameters where proposed moves are accepted if simulated data is similar to observed. Population Monte Carlo and ABC-MCMC improve on rejection sampling by using sequential importance sampling and MCMC moves to propose parameters in high density regions.
![Columbia workshop [ABC model choice]](https://cdn.slidesharecdn.com/ss_thumbnails/columbia-110924060002-phpapp01-thumbnail.jpg?width=640&height=640&fit=bounds)
