Approximate Bayesian computation for discretely observed continuous time multi-state models
Inference for continuous time multi-state models presents considerable computational difficulties when the process is only observed at discrete time points with no additional information about the state transitions. In fact, for general multi-state Markov model, the evaluation of the likelihood function is possible only via intensive numerical approximations.