000			01918 a2200313 4500
003			Nust
005			20220801124027.0
010			_a2009008615
020			_a0-262-01319-3 (hardcover : alk. paper)
020			_a978-0-262-01319-2 (hardcover : alk. paper)
035			_a(OCoLC)ocn311310322
035			_aDAW12537823
040			_cNust
082	0	0	_a519.54202,KOL
100	1		_aKoller, Daphne _996035
245	1	0	_aProbabilistic Graphical Models : _bPrinciples and Techniques / _cDaphne Koller, Nir Friedman
260			_aCambridge, MA : _bMIT Press, _ccop. 2009
300			_axxxv, 1231 s. : _bill.
490	0		_aAdaptive computation and machine learning
505	8		_aIntroduction (Page-1), Foundations (Page-15), Bayesian Network Representation (Page-45), Undirected Graphical Models (Page-103), Local Probabilistic Models (Page-157), Template-Based Representations (Page-199), Gaussian Network Models (Page-247), Exponential Family (Page-261), Exact Inference: Variable Elimination (Page-287), Exact Inference: Clique Trees (Page-345), Inference as Optimization (Page-381), Particle-Based Approximate Inference (Page-487), MAP Inference (Page-551), Inference in Hybrid Networks (Page-605), Inference in Temporal Models (Page-651), Learning Graphical Models: Overview (Page-697), Parameter Estimation (Page-717), Structure Learning in Bayesian Networks (Page-783), Partially Observed Data (Page-849), Learning Undirected Models (Page-943), Causality (Page-1009), Utilities and Decisions (Page-1059), Structured Decision Problems (Page-1085),
650		0	_aBayesian statistical decision theory _xGraphic methods. _996036
650		0	_aBeslutsteori _xmatematisk statistik _996037
650		0	_aGraphical modeling (Statistics) _996038
650		0	_aProbability _9225
650		0	_aSannolikhetskalkyl _995846
650		0	_aStatistisk inferens _996039
700	1		_aFriedman, Nir _996040
942			_2ddc _cREF
999			_c190477 _d190477