Many controlled processes, such as biochemical ones, are repetitive, similar to batch-organized processes. They generate Optimal Control Problems (OCPs) solved by optimal controllers, which often ...

What is a Gaussian Graphical Model ? A Gaussian graphical model captures conditional (in)dependencies among a set of variables. These are pairwise relations (partial correlations) controlling for the ...

Abstract: Bayesian optimization (BO) is a versatile and robust global optimization method under uncertainty. However, most of the BO algorithms were developed for problems with only continuous ...

Abstract: Learning the structure of Bayesian networks (BNs) from high dimensional discrete data is common nowadays but a challenging task, due to the large parameter space, the acyclicity constraint ...

I was halfway through a master’s in Computer Science when my vision changed. I was working as a data scientist during my summer off from school, and I had friends who said things like “I’m at a local ...

System identification learns models of dynamical systems from input–output measurements. Estimated models should generalize by predicting system’s output responses to new, previously unseen inputs.

Dynamical systems have been used to model the paroxysmal nature of seizures in epilepsy. In parallel, seizures have been discovered to occur with cyclical periodicity in epilepsy, occurring at ...

Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are ...