| Approximate Bayesian Computation (ABC) methods can be used in situations where the evaluation of the likelihood is computationally prohibitive. They are thus ideally suited for analyzing the complex dynamical models encountered in systems biology, where knowledge of the full (approximate) posterior is often essential.

This talk gives an overview of an ABC algorithm based on Sequential Monte Carlo (ABC SMC). Different uses of the algorithm will be presented, depending on the application question of interest. The first is the general parameter estimation framework, where the interest lies in estimating the posterior parameter distribution from available experimental data. In the second context we ask whether the model can... |
