Bayesian Reasoning and Machine Learning/ By David Barber
Material type:
TextPublication details: New Delhi: Cambridge University Press, 2012.Description: 697P;, xxiv, 24cmISBN: - 9781107439955
- 23rd Ed. 006.31 BAR
| Item type | Current library | Collection | Call number | Status | Barcode | |
|---|---|---|---|---|---|---|
Books
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Central Library | Computer Science | 006.31 BAR (Browse shelf(Opens below)) | Available | 4398 |
CONTENTS
Preface
List of Notation
BRML TOOLBOX
I Inference in Probabilistic Models
1. Probabilistic Reasoning.
2. Basic Graph Concepts.
3. Belief Networks.
4. Graphical Models.
5. Efficient Inference in Trees.
6. The Junction Tree Algorithm.
7. Making Decisions.
II Learning in Probabilistic Model
8. Statistics for Machine Learning.
9. Learning as Inference as Inference.
10. Naive Bayes.
11. Learning With Hidden Variables.
12. Bayesian Model Selection.
III Machine Learning
13. Machine Learning Concepts
14. Nearest Neighbour Classification.
15. unsupervised Linear Dimension Reduction.
16. Supervised Linear Dimension reduction.
17. Linear Models.
18. Bayesian Linear Models.
19. Gaussian Processes.
20. Mixture Models.
21. Latent Linear Models.
22. Latent Ability Models.
IV Dynamical Models.
23. Discrete-State Markov Models.
24. Continuous-State Markov Models.
25. Switching Linear Dynamical Systems.
26. Distributed Computation.
V Approximate Interference.
27. Sampling.
28. Deterministic Approximate Inference.
Includes Appendix, Reference and Index.
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