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Optimized Bayesian Dynamic Advising Theory & Algorithms HC
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“Springer Verlag; London, 2006. Hardcover. A Near Fine, binding firm, interior and extremities tidy, ”... Read moreabout condition
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Located in: Foster, Rhode Island, United States
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Estimated between Thu, 13 Jun and Sat, 15 Jun to 43230
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eBay item number:125744008257
Item specifics
- Condition
- Like New
- Seller Notes
- ISBN
- 9781852339289
- Subject Area
- Computers, Mathematics
- Publication Name
- Optimized Bayesian Dynamic Advising : Theory and Algorithms
- Item Length
- 9.3 in
- Publisher
- Springer London, The Limited
- Subject
- User Interfaces, Computer Science, Probability & Statistics / Bayesian Analysis, Social Aspects / Human-Computer Interaction
- Series
- Advanced Information and Knowledge Processing Ser.
- Publication Year
- 2005
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Width
- 6.1 in
- Item Weight
- 73 Oz
- Number of Pages
- Xvii, 529 Pages
About this product
Product Information
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 and layout . . . . . . . . . . . . . . . . . . . 7 1. 5 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Underlying theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. 1 General conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. 2 Basic notions and notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Product Identifiers
Publisher
Springer London, The Limited
ISBN-10
1852339284
ISBN-13
9781852339289
eBay Product ID (ePID)
45695672
Product Key Features
Publication Name
Optimized Bayesian Dynamic Advising : Theory and Algorithms
Format
Hardcover
Language
English
Subject
User Interfaces, Computer Science, Probability & Statistics / Bayesian Analysis, Social Aspects / Human-Computer Interaction
Series
Advanced Information and Knowledge Processing Ser.
Publication Year
2005
Type
Textbook
Subject Area
Computers, Mathematics
Number of Pages
Xvii, 529 Pages
Dimensions
Item Length
9.3 in
Item Width
6.1 in
Item Weight
73 Oz
Additional Product Features
LCCN
2005-923319
Intended Audience
Scholarly & Professional
Number of Volumes
1 Vol.
Lc Classification Number
Qa75.5-76.95
Table of 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.
Copyright Date
2006
Dewey Decimal
519.5/42
Dewey Edition
22
Illustrated
Yes
Item description from the seller
Seller assumes all responsibility for this listing.
eBay item number:125744008257
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Foster, Rhode Island, United States
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