Optimized Bayesian Dynamic Advising Theory and Algorithms.

• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: London : Springer London, 2006.
• Series: Advanced Information and Knowledge Processing
• Subjects: Computer science.; Computers.; User interfaces (Computer systems).; Artificial intelligence.; Computer simulation.; Pattern recognition.; Statistics.; Computer Science.; Models and Principles.; User Interfaces and Human Computer Interaction.; Artificial Intelligence (incl. Robotics).; Simulation and Modeling.; Pattern Recognition.; Statistics and Computing/Statistics Programs.

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...

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.