- Full Description
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, the book 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.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.
- Table of Contents
Table of Contents
- Underlying Theory.
- Approximate and Feasible Learning.
- Approximate Design.
- Problem Formulation.
- Solution and Principles of its Approximation: Learning.
- Solution and Principles of its Approximation: Design.
- Learning with Normal Factors and Components.
- Design with Normal Mixtures.
- Learning with Markov Chain Factors and Components.
- Design with Markov Chain Mixtures.
- Sandwich BMTB for Mixture Initiation.
- Mixed Mixtures.
- Applications of the Advisory System.
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