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Optimized Bayesian Dynamic Advising

Theory and Algorithms

  • Book
  • © 2006

Overview

Editors:
  1. Miroslav Karny

    1. Dept. Adaptive Systems, ASCR Praha Inst. Information Theory & Automation, Praha, Czech Republic

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  • Provides a generic methodology with elaborated algorithmic image of probabilistic, possibly adaptive, optimised advisory system supporting dynamic decision making under uncertainty in a complex environment
  • Dynamic, adaptive, mixture modelling of non-linear uncertain systems from le6 data records, each having several tens of entries, has not been done before
  • Optimization of advises in a fully probabilistic sense has not been done before
  • Brings a completely new treatment of the topic of supervisory control of nonlinear uncertain systems to the fore
  • Neither book nor solution, have a viable competitor
  • Original problem formulation and practical solution of the optimised and adaptive advising
  • Many particular, often novel, results widely applicable in signal processing, modelling and estimation of non-linear systems, multi-step prediction, pattern recognition and (adaptive) control
  • Diverse application potential from technological processes, medical diagnostics, control of urban traffic to economical and societal processes
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advanced Information and Knowledge Processing (AI&KP)

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Table of contents (15 chapters)

  1. Front Matter

    Pages I-XV
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  2. Introduction

    Pages 1-10

  3. Underlying theory

    Pages 11-41

  4. Approximate and feasible learning

    Pages 43-56

  5. Approximate design

    Pages 57-65

  6. Problem formulation

    Pages 67-94

  7. Solution and principles of its approximation: learning part

    Pages 95-192

  8. Solution and principles of its approximation: design part

    Pages 193-241

  9. Learning with normal factors and components

    Pages 243-308

  10. Design with normal mixtures

    Pages 309-376

  11. Learning with Markov-chain factors and components

    Pages 377-410

  12. Design with Markov-chain mixtures

    Pages 411-435

  13. Sandwich BMTB for mixture initiation

    Pages 437-461

  14. Mixed mixtures

    Pages 463-479

  15. Applications of the advisory system

    Pages 481-506

  16. Concluding remarks

    Pages 507-509

  17. Back Matter

    Pages 511-529
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Keywords

About this book

This work summarizes the theoretical and algorithmic basis of optimized pr- abilistic advising. It developed from a series of targeted research projects s- ported both by the European Commission and Czech grant bodies. The source text has served as a common basis of communication for the research team. When accumulating and re?ning the material we found that the text could also serve as • a grand example of the strength of dynamic Bayesian decision making, • a practical demonstration that computational aspects do matter, • a reference to ready particular solutions in learning and optimization of decision-making strategies, • a source of open and challenging problems for postgraduate students, young as well as experienced researchers, • a departure point for a further systematic development of advanced op- mized advisory systems, for instance, in multiple participant setting. These observations have inspired us to prepare this book. Prague, Czech Republic Miroslav K´ arn´ y October 2004 Josef B¨ ohm Tatiana V. Guy Ladislav Jirsa Ivan Nagy Petr Nedoma Ludv´ ?k Tesa? r Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. 1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. 2 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. 2. 1 Operator supports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. 2. 2 Mainstream multivariate techniques . . . . . . . . . . . . . . . . . 4 1. 2. 3 Probabilistic dynamic optimized decision-making . . . . . . 6 1. 3 Developed advising and its role in computer support . . . . . . . . . 6 1. 4 Presentation style, readership andlayout . . . . . . . . . . . . . . . . . . . 7 1. 5 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Underlying theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. 1 General conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. 2 Basic notions and notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Editors and Affiliations

  • Dept. Adaptive Systems, ASCR Praha Inst. Information Theory & Automation, Praha, Czech Republic

    Miroslav Karny

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