David Barber
Bayesian Reasoning and Machine Learning
http://www.cambridge.org/gb/knowledge/isbn/item6222196/Bayesian%20Reasoning%20and%20Machine%20Learning/?site_locale=en_GB
- ISBN:9780521518147
- Publication date:February 2012
Table of Contents Preface
Part 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
Part II. Learning in Probabilistic Models: 8. Statistics for machine learning
9. Learning as inference
10. Naive Bayes
11. Learning with hidden variables
12. Bayesian model selection
Part 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
Part IV. Dynamical Models: 23. Discrete-state Markov models
24. Continuous-state Markov models
25. Switching linear dynamical systems
26. Distributed computation
Part V. Approximate Inference: 27. Sampling
28. Deterministic approximate inference
Appendix. Background mathematics
Bibliography
Index.
120211.zip
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