Materials from our IICD workshop on parameter inference are freely available!

Khanh and Yanjie led the IICD Parameter Estimation Workshop, where we discussed in detail the different approaches to perform parameter inference for data-driven mathematical modeling. The topics included least-square inference for deterministic models, likelihood-based Markov chain Monte Carlo for stochastic frameworks, and Approximate Bayesian Computation (ABC) for likelihood-free parameter estimation. For ABC, we analyzed and compared the rejection method, sequential Monte Carlo (SMC), and finally ABC-SMC via random forest (ABC-SMC-RF), a new algorithm that we developed recently and is available on Github.

The coding materials for interactive sessions and presentation slides are freely available on our Github repository. Feel free to contact us for more information!