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130626 ||| eng |
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|a 9781846282546
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| 100 |
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|a Karny, Miroslav
|e [editor]
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| 245 |
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|a Optimized Bayesian Dynamic Advising
|h Elektronische Ressource
|b Theory and Algorithms
|c edited by Miroslav Karny
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| 250 |
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|a 1st ed. 2006
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| 260 |
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|a London
|b Springer London
|c 2006, 2006
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| 300 |
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|a XVII, 529 p
|b online resource
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| 505 |
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|a 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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| 653 |
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|a User interfaces (Computer systems)
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|a Models of Computation
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|a Computer science
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|a Computer simulation
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computer Modelling
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| 653 |
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|a Artificial intelligence
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|a Mathematical statistics / Data processing
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a User Interfaces and Human Computer Interaction
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| 653 |
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|a Statistics and Computing
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|a Human-computer interaction
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| 653 |
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|a Pattern recognition systems
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Advanced Information and Knowledge Processing
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| 028 |
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|a 10.1007/1-84628-254-3
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| 856 |
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|u https://doi.org/10.1007/1-84628-254-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 004.0151
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| 520 |
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|a 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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