000			01605nam a22001817a 4500
003			OSt
005			20241104140852.0
008			241104b |||||||| |||| 00| 0 eng d
020			_a9781107439955
040			_beng _cNLU
082			_223rd Ed. _a006.31 _bBAR
100			_aBarber, David
245			_aBayesian Reasoning and Machine Learning/ _cBy David Barber
260			_a New Delhi: _bCambridge University Press, _c2012.
300			_a697P;, _bxxiv, _c24cm.
500			_aCONTENTS Preface List of Notation BRML TOOLBOX 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. II Learning in Probabilistic Model 8. Statistics for Machine Learning. 9. Learning as Inference as Inference. 10. Naive Bayes. 11. Learning With Hidden Variables. 12. Bayesian Model Selection. 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. IV Dynamical Models. 23. Discrete-State Markov Models. 24. Continuous-State Markov Models. 25. Switching Linear Dynamical Systems. 26. Distributed Computation. V Approximate Interference. 27. Sampling. 28. Deterministic Approximate Inference. Includes Appendix, Reference and Index.
942			_2ddc _cBK _n0
999			_c13378 _d13378