A state-of-the-art research monograph providing consistent treatment of supervisory control, by one of the world s leading groups in the area of Bayesian identification, control, and decision making.
A state-of-the-art research monograph providing consistent treatment of supervisory control, by one of the world s leading groups in the area of Bayesian identification, control, and decision making. Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Miroslav Karny, Academy of Sciences of the Czech Republic, Prague, Czech Republic
Inhaltsangabe
Introduction.- 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.- Conclusions.- References.- Index.
Underlying theory.- Approximate and feasible learning.- Approximate design.- Problem formulation.- Solution and principles of its approximation: learning part.- Solution and principles of its approximation: design part.- 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.- Concluding remarks.
Introduction.- 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.- Conclusions.- References.- Index.
Underlying theory.- Approximate and feasible learning.- Approximate design.- Problem formulation.- Solution and principles of its approximation: learning part.- Solution and principles of its approximation: design part.- 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.- Concluding remarks.
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