Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning
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ISBN-13:
9780521518147
Erscheinungsdatum:
02.02.2012
Seiten:
708
Autor:
David Barber
Gewicht:
1432 g
Format:
250x175x43 mm
Sprache:
Englisch

Inhaltsverzeichnis
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.
Beschreibung
A practical introduction for final-year undergraduate and graduate level students without a solid background in linear algebra and calculus.
Autor
David Barber is Reader in Information Processing in the Department of Computer Science, University College London.

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