01903nam a2200253 a 4500 vtls000070595 KUKTEM 20251125095539.0 130605t2012 enka f 001 0 eng d 9780521518147 201403051239 saini 201306051041 hairil UMP QA267 .B37 2012 Barber, David Bayesian reasoning and machine learning / David Barber Cambridge : Cambridge University Press, 2012 xxiv, 697 p. : ill. ; 25 cm. Includes bibliographical references and index 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 Machine learning Bayesian statistical decision theory 0 0 0 0 20000 20000 2019-09-04 281.21 1 QA267 .B37 2012 0000081697 2022-07-05 00:00:00 2022-04-21 1 281.21 2019-09-04 OPEN VIRTUA40 72449 72455 VTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5040*5050*6500*6501*9992 1