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02160nam a2200241 a 4500 |
| 001 |
1969926 |
| 005 |
20171111235900.0 |
| 008 |
121220s2012 r da erci 001 u eng d |
| 020 |
|
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|a 9780521518147
|q hbk.
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| 040 |
|
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|a CY
|b Βιβλιοθήκη Ανοικτού Πανεπιστημίου Κύπρου
|e AACR2
|
| 050 |
|
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|a QA267.B347 2012
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| 100 |
1 |
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|a Barber, David,
|d 1968-
|
| 245 |
1 |
0 |
|a Bayesian reasoning and machine learning/
|c David Barber
|
| 260 |
1 |
0 |
|a Cambridge ;
|b Cambridge University Press,
|c c2012
|a New York:
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| 300 |
1 |
0 |
|a xxiv, 697 p. :
|b ill. ;
|c 26 cm.
|
| 504 |
1 |
0 |
|a Includes bibliographical references and index.
|
| 505 |
1 |
0 |
|a Machine generated contents note: 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.
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| 650 |
1 |
0 |
|a Machine learning
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| 650 |
1 |
0 |
|a Bayesian statistical decision theory
|
| 952 |
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