Carlo Albert

In essentially all applied sciences, data-driven modeling heavily relies on a sound calibration of model parameters to measured data for understanding the underlying mechanisms that lead to observed features. Solar dynamo models are no exception. Bayesian statistics is a consistent framework for parameter inference where knowledge about model parameters is expressed through probability distributions and updated using measured data. However, Bayesian inference with non-linear stochastic models can become computationally extremely expensive and it is therefore hardly ever applied. In recent years, sophisticated and scalable algorithms have emerged, which have the potential of making Bayesian inference for stochastic models feasible. We investigate the power of Approximate Baysian Computation (ABC), enhanced by Machine Learning methods, and Hamiltonian Monte Carlo algorithms applied to solar dynamo models.