Ebook: Optimized Bayesian Dynamic Advising: Theory and Algorithms

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.

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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.

---

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.

Content:
Front Matter....Pages I-XV
Introduction....Pages 1-10
Underlying theory....Pages 11-41
Approximate and feasible learning....Pages 43-56
Approximate design....Pages 57-65
Problem formulation....Pages 67-94
Solution and principles of its approximation: learning part....Pages 95-192
Solution and principles of its approximation: design part....Pages 193-241
Learning with normal factors and components....Pages 243-308
Design with normal mixtures....Pages 309-376
Learning with Markov-chain factors and components....Pages 377-410
Design with Markov-chain mixtures....Pages 411-435
Sandwich BMTB for mixture initiation....Pages 437-461
Mixed mixtures....Pages 463-479
Applications of the advisory system....Pages 481-506
Concluding remarks....Pages 507-509
Back Matter....Pages 511-529

---

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.

Content:
Front Matter....Pages I-XV
Introduction....Pages 1-10
Underlying theory....Pages 11-41
Approximate and feasible learning....Pages 43-56
Approximate design....Pages 57-65
Problem formulation....Pages 67-94
Solution and principles of its approximation: learning part....Pages 95-192
Solution and principles of its approximation: design part....Pages 193-241
Learning with normal factors and components....Pages 243-308
Design with normal mixtures....Pages 309-376
Learning with Markov-chain factors and components....Pages 377-410
Design with Markov-chain mixtures....Pages 411-435
Sandwich BMTB for mixture initiation....Pages 437-461
Mixed mixtures....Pages 463-479
Applications of the advisory system....Pages 481-506
Concluding remarks....Pages 507-509
Back Matter....Pages 511-529
....