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Optimized Bayesian Dynamic Advising: Theory and Algorithms

This work summarizes theoretical & algorithmic basis of optimised probabilistic advising & crowns more than 3 decades of research endeavour of one of the world?s leading groups in the area of Bayesian identification, control & decision making.

This book comprises a unified consistent treatment of an important problem of the design in advisory systems serving supervisors of complex processes. The research was motivated by the urgent need for industrial producers to minimize the down time caused by process malfunction & improper maintenance. The developed probabilistic mining of information hidden within acquired data & the subsequent dynamic optimisation of decision strategies can be used in many applications (medical, societal, economical, etc).

Additional value arises from scientific treatment of the role of the operator in process supervision thus making a significant contribution to the design of anthropocentric automation systems.

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