I argue why hierarchical Bayesian models are a good fit for real-world applications that rely on trustworthy and interpretable ML. By means of two industrial examples I show how such hierarchical models can be formulated from expert knowledge.
Research Associate at the University of Cambridge and Research Scientist at Siemens AG. I am interested in scalable Bayesian machine learning and Gaussian processes.