Jamie Owen (Newcastle University, UK): Scalable inference for intractable Markov processes using ABC and pMCMC

Bayesian inference for non-linear stochastic processes has become of increasing interest in recent years. Problems of this type typically have intractable likelihoods, and prior knowledge about model rate parameters is often poor. MCMC techniques can lead to ‘exact’ inference for such models, but in practice can suffer performance issues such as long burn-in periods, slow mixing and poor amenability to parallelisation. On the other hand approximate Bayesian computation (ABC) techniques can allow rapid, concurrent exploration of a large parameter space but yield only approximate posterior distributions. Here we consider the combined use of ABC and more standard MCMC techniques for improved computational efficiency which still allow ‘exact’ posterior inference and effective use of parallel hardware.

Keywords: approximate Bayesian computation, ABC, MCMC, Markov processes, stochastic networks

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