BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime
Martin Fitzner, Adrian Šošić, Alexander V. Hopp, Marcel Müller, Rim Rihana, Karin Hrovatin, Fabian Liebig, Mathias Winkel, Wolfgang Halter, Jan Gerit Brandenburg
Abstract
The Bayesian Back End (BayBE) has a range of advanced features enabling scientists to go beyond the basic Bayesian optimization loop and readily tackle real world experimental campaigns.
Topics & Concepts
Bayesian probabilityComputer scienceEnvironmental scienceArtificial intelligenceMachine Learning in Materials ScienceStatistical Methods in Clinical TrialsComputational Drug Discovery Methods