Abstract
Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor. Markov Chain Monte Carlo (MCMC) techniques can lead to exact inference in such models but in practice can suffer performance issues including long burn-in periods and poor mixing. On the other hand approximate Bayesian computation techniques can allow rapid exploration of a large parameter space but yield only approximate posterior distributions. Here we consider the combined use of approximate Bayesian computation and MCMC techniques for improved computational efficiency while retaining exact inference on parallel hardware.
Highlights
Stochastic kinetic models describe the probabilistic evolution of a dynamical system
Since it is typically not possible to initialise a Markov Chain Monte Carlo (MCMC) chain with a draw from the desired target, we propose an approach to parallel MCMC by choosing initial parameter vectors according to samples from an approximate posterior distribution
We have proposed an approach to inference for Markov processes that is asymptotically exact and combines the relative strengths of Approximate Bayesian computation (ABC) and particle Markov chain Monte Carlo (pMCMC) methodology to increase computational efficiency through use of parallel hardware
Summary
Stochastic kinetic models describe the probabilistic evolution of a dynamical system.
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