Likelihood-free inference and approximate Bayesian computation for stochastic modelling
With increasing model complexity, sampling from the posterior distribution in a Bayesian context becomes challenging. The reason might be that the likelihood function is analytically unavailable or computationally costly to evaluate. In this thesis a fairly new scheme called approximate Bayesian computation is studied which, through simulations from the likelihood function, approximately simulates