Learning from data in fundamental physics research

Bayesian statistics allows to quantify the strength of inductive inference from facts (such as experimental data) to propositions such as scientific hypotheses and models. It can be used for example in searches for physics beyond the standard model, and in multi-messenger astronomy for the analysis of gravitational wave signals. In our theoretical-physics research we develop novel machine-learning methods to make the Bayesian approach tractable for models that involve heavy computing. A general message of this presentation will be that the Bayesian philosophy can be embraced by machine-learning practitioners to introduce uncertainties and to avoid overfitting.