02155nam a22002057a 450000500170000000800410001702000180005802800980007608200150017410000170018924500590020626000650026530000310033036500090036150510600037052004220143065000210185265500590187370000170193220200306101035.0200306b           ||||| |||| 00| 0 eng d  a9781107439955  aBrijwasi Book DistributorsbH-87, Lalita Park,Laxmi Nagar,Delhi-110092cBW/005744d28/02/2020  a006.31 BAR  aDavid Barber  aBayesian reasoning and machine learningc/David Barber  aCambridge ; New York b: Cambridge University Pressc, 2012.  axxiv, 697 pages c ; 26 cm  b1495  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.  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  aComputer Science  aMachine learningbBayesian statistical decision theory  aDavid Barber