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stat.CO2019
Markov chain Monte Carlo importance samplers for Bayesian models with intractable likelihoods
Jordan Franks
We consider the efficient use of an approximation within Markov chain Monte Carlo (MCMC), with subsequent importance sampling (IS) correction of the Markov chain inexact output, le…
stat.CO2019
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
Matti Vihola, Jordan Franks
Approximate Bayesian computation allows for inference of complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo impleme…