Written by one of the world’s leading groups in the area of Bayesian identification, control and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising.
Starting from abstract ideas and formulations, and culminating in detailed algorithms, Optimized Bayesian Dynamic Advising comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization. The proposed non-standard problem formulation and its solution mark a significant contribution to the design of anthropocentric automation systems.
Written for a broad audience, including developers of algorithms and application engineers, researchers, lecturers and postgraduates, this book can be used as a reference tool, and an advanced text on Bayesian dynamic decision making.

Provides a generic methodology with elaborated algorithmic image of probabilistic, possibly adaptive, optimised advisory system supporting dynamic decision making under uncertainty in a complex environment Dynamic, adaptive, mixture modelling of non-linear uncertain systems from le6 data records, each having several tens of entries, has not been done before Optimization of advises in a fully probabilistic sense has not been done before Brings a completely new treatment of the topic of supervisory control of nonlinear uncertain systems to the fore Neither book nor solution, have a viable competitor Original problem formulation and practical solution of the optimised and adaptive advising Many particular, often novel, results widely applicable in signal processing, modelling and estimation of non-linear systems, multi-step prediction, pattern recognition and (adaptive) control Diverse application potential from technological processes, medical diagnostics, control of urban traffic to economical and societal processes Includes supplementary material: sn.pub/extras