
Optimized Bayesian Dynamic Advising
Theory and Algorithms
Miroslav Karny(Editor)
Springer (Publisher)
Published on 10. October 2005
Book
Hardback
XVII, 529 pages
978-1-85233-928-9 (ISBN)
Description
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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
More details
Series
Edition
2006 edition
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Research
Product notice
Unsewn / adhesive bound
Cloth over boards
Illustrations
XVII, 529 p.
Dimensions
Height: 245 mm
Width: 162 mm
Thickness: 32 mm
Weight
931 gr
ISBN-13
978-1-85233-928-9 (9781852339289)
DOI
10.1007/1-84628-254-3
Schweitzer Classification
Other editions
Additional editions

Book
10/2014
Springer
€160.49
Shipment within 15-20 days

E-Book
12/2005
Springer
€149.79
Available for download
Content
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.