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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, Optim...

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Bibliographic Details
Other Authors: Karny, Miroslav (Editor)
Format: eBook
Language:English
Published: London Springer London 2006, 2006
Edition:1st ed. 2006
Series:Advanced Information and Knowledge Processing
Subjects:
User Interfaces (computer Systems)
Models Of Computation
Computer Science
Computer Simulation
Artificial Intelligence
Computer Modelling
Mathematical Statistics / Data Processing
Automated Pattern Recognition
User Interfaces And Human Computer Interaction
Statistics And Computing
Human-computer Interaction
Pattern Recognition Systems
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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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