Bayesian inference for Markov jump processes with informative observations

Understand

In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models.

• Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC.
• Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process.
• When observations are highly informative, use of the forward simulator is likely to be inefficient and may even preclude an exact (simulation based) analysis.