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 Kn arnn y October 2004 Josef B· ohm Tatiana V. Guy Ladislav Jirsa Ivan Nagy Petr Nedoma Ludvn ?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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
| ISBN: | 9781447156758 |
| Publication date: | 20th October 2014 |
| Author: | Miroslav Karny |
| Publisher: | Springer an imprint of Springer London |
| Format: | Paperback |
| Pagination: | 529 pages |
| Series: | Advanced Information and Knowledge Processing |
| Genres: |
Mathematical theory of computation Pattern recognition Human–computer interaction Computer modelling and simulation Artificial intelligence Probability and statistics Mathematical and statistical software |
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.
Optimized Bayesian Dynamic Advising features in the following genres: Mathematical theory of computation, Pattern recognition, Human–computer interaction, Computer modelling and simulation, Artificial intelligence, Probability and statistics, Mathematical and statistical software
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Optimized Bayesian Dynamic Advising was written by Miroslav Karny and published by Springer an imprint of Springer London
Optimized Bayesian Dynamic Advising has 529 pages
Yes it is part of Advanced Information and Knowledge Processing series