Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design

no code implementations • 16 Dec 2019 • Piyush Pandita, Nimish Awalgaonkar, Ilias Bilionis, Jitesh Panchal

We model the underlying information source as a fully-Bayesian, non-stationary Gaussian process (FBNSGP), and derive an approximation of the information gain of a hypothetical experiment about an arbitrary QoI conditional on the hyper-parameters The EIG about the same QoI is estimated by sample averages to integrate over the posterior of the hyper-parameters and the potential experimental outcomes.

Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints

no code implementations • 21 Mar 2019 • Nimish Awalgaonkar, Ilias Bilionis, Xiaoqi Liu, Panagiota Karava, Athanasios Tzempelikos

The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction.

Active Learning Bayesian Inference