Optimized Bayesian Dynamic Advising
1. Introduction
2. Underlying theory
3. Approximate and feasible learning
4. Approximate design
5. Problem formulation
6. Solution and principles of its approximation: learning part
7. Solution and principles of its approximation: design part
8. Learning with normal factors and components
9. Design with normal mixtures
10. Learning with Markov-chain factors and components
11. Design with Markov-chain mixtures
12. Sandwich BMTB for mixture initiation
13. Mixed mixtures
14. Applications of the advisory system
15. Concluding remarks
DRM-restrictions
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Keywords: COMPUTERS / Computer Science COM014000
- Publisher
- Springer
- Publication year
- 2006
- Language
- en
- Edition
- 1
- Category
- Information Technology, Telecommunications
- Format
- Ebook
- eISBN (PDF)
- 9781846282546