000			01903nam a2200253 a 4500
001			vtls000070595
003			KUKTEM
005			20251125095539.0
008			130605t2012 enka f 001 0 eng d
020			_a9780521518147
039		9	_a201403051239 _bsaini _y201306051041 _zhairil
040			_aUMP
090			_aQA267 .B37 2012
100	1		_aBarber, David
245	1	0	_aBayesian reasoning and machine learning / _cDavid Barber
260			_aCambridge : _bCambridge University Press, _c2012
300			_axxiv, 697 p. : _bill. ; _c25 cm.
504			_aIncludes bibliographical references and index
505	0		_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
650		0	_aMachine learning
650		0	_aBayesian statistical decision theory
999			_aVIRTUA40 _c72449 _d72455
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5040*5050*6500*6501*9992
942	0	0	_01