01667nam a2200193 a 4500001001400000003000700014005001700021008004100038020001800079040000800097100001800105245006000123260005100183300003500234504005000269505109200319650002101411650004101432vtls000070595KUKTEM20251125095539.0130605t2012    enka   f      001 0 eng d  a9780521518147  aUMP1 aBarber, David10aBayesian reasoning and machine learning /cDavid Barber  aCambridge :bCambridge University Press,c2012  axxiv, 697 p. :bill. ;c25 cm.  aIncludes bibliographical references and index0 aMachine 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 0aMachine learning 0aBayesian statistical decision theory