PhD defence by Ola Rønning
Summary
We also present a ready-to-use library for inference with Stein mixtures as an extension to the NumPyro probabilistic programming language (PPL). The library, called EinStein, includes the black box Stein mixture inference engine, automatic guide generation, many studied kernels, and copiable examples of Bayesian neural networks and deep Markov models. In the second part of the thesis, we study the protein structure prediction problem as a showcase for applying PPLs in the natural sciences. A high-fidelity solution to the problem could have a massive impact on treatment for misfolding diseases such as cancer, Alzheimers, Huntingtons, and Parkinsons. Our model captures probable angle pairs for each amino acid order of magnitude faster than preexisting methods.