000			01863cam a2200241 a 4500
999			_c51460 _d53007
003			ISURa
008			090227s2009 maua b 001 0 eng
020			_a9780262013192 (hardcover : alk. paper)
041			_aEnglish
082	0	0	_a519.5420285 _bKOL
100	1		_aKoller, Daphne _972627
245	1	0	_aProbabilistic graphical models _bprinciples and techniques
260			_aCambridge, MA _bMIT Press _c2009
300			_axxi, 1231 p. _c24 cm.
440			_aAdaptive computation and machine learning. _969132
500			_a 1. Introduction -- 2. Foundations -- I. Representation -- 3. Bayesian Network Representation -- 4. Undirected Graphical Models -- 5. Local Probabilistic Models -- 6. Template-Based Representations -- 7. Gaussian Network Models -- 8. Exponential Family -- II. Inference -- 9. Exact Inference: Variable Elimination -- 10. Exact Inference: Clique Trees -- 11. Inference as Optimization -- 12. Particle-Based Approximate Inference -- 13. MAP Inference -- 14. Inference in Hybrid Networks -- 15. Inference in Temporal Models -- III. Learning -- 16. Learning Graphical Models: Overview -- 17. Parameter Estimation -- 18. Structure Learning in Bayesian Networks -- 19. Partially Observed Data -- 20. Learning Undirected Models -- IV. Actions and Decisions -- 21. Causality -- 22. Utilities and Decisions -- 23. Structured Decision Problems -- 24. Epilogue -- A. Background Material.
520			_aA general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.
650		0	_aGraphical modeling (Statistics) _972628
650		0	_aBayesian statistical decision theory _xGraphic methods. _972629
650		0	_aModèles graphiques (Statistique) _972630
700	1		_aFriedman, Nir _972631
942			_2ddc _cLN