Probabilistic Graphical Models : Principles and Techniques / Daphne Koller, Nir Friedman
Series: Adaptive computation and machine learningPublisher: Cambridge, MA : MIT Press, cop. 2009Description: xxxv, 1231 s. : illISBN: 0-262-01319-3 (hardcover : alk. paper); 978-0-262-01319-2 (hardcover : alk. paper)Subject(s): Bayesian statistical decision theory -- Graphic methods | Beslutsteori -- matematisk statistik | Graphical modeling (Statistics) | Probability | Sannolikhetskalkyl | Statistisk inferensDDC classification: 519.54202,KOL| Item type | Current location | Home library | Shelving location | Call number | URL | Status | Notes | Date due | Barcode | Item holds |
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Reference
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Military College of Signals (MCS) | Military College of Signals (MCS) | Reference | 519.54202,KOL (Browse shelf) | Link to resource | Not for loan | Almirah No.19, Shelf No.5 | MCS36065 |
Introduction (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),
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