BIO：Biao Huang Ph.D., PEng, FCAE, FCIC, ProfessorDepartment of Chemical and Materials EngineeringUniversity of AlbertaCanada

ABSTRACT：Bayesian theory, due to its mathematical rigor and application flexibility, has attracted great interests from both academia and practitioners. The original Bayesian rule, as a single formula, can evolve into pages of long mathematical derivations. Yet the end result provides very meaningful solutions to the practical problems. Although the control community may not be very familiar with the term “Bayesian”, it has been adopted by control scientists as early as the start of modern control. The most well known application of Bayesian theory in control engineering is Kalman filter which has been widely adopted by the control community. It is now commonly recognized that many control related problems can be formulated under Bayesian framework and readily solved. Bayesian inference is getting even more popular due to the growing interest in Big Data and Data Analytics. This presentation will give a historical overview of Bayesian methods in control engineering, current activities, and future trends. These will include Bayesian methods for modeling, estimation, fault detection & isolation, causality analysis, control performance monitoring, and soft sensors development. A toolbox for process data analytics will also be demonstrated.