| 000 | 02205nam a22002297a 4500 | ||
|---|---|---|---|
| 999 |
_c56469 _d56469 |
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| 005 | 20200306101035.0 | ||
| 008 | 200306b ||||| |||| 00| 0 eng d | ||
| 020 | _a9781107439955 | ||
| 028 |
_aBrijwasi Book Distributors _bH-87, Lalita Park,Laxmi Nagar,Delhi-110092 _cBW/005744 _d28/02/2020 |
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| 082 | _a006.31 BAR | ||
| 100 | _aDavid Barber | ||
| 245 |
_aBayesian reasoning and machine learning _c/David Barber |
||
| 260 |
_aCambridge ; New York _b: Cambridge University Press _c, 2012. |
||
| 300 |
_axxiv, 697 pages _c ; 26 cm |
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| 365 | _b1495 | ||
| 505 | _aPreface; 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. | ||
| 520 | _aMachine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science | ||
| 650 | _aComputer Science | ||
| 655 |
_aMachine learning _bBayesian statistical decision theory |
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| 700 | _aDavid Barber | ||
| 942 | _cBOOK | ||