Probabilistic Graphical Models: 7th European Workshop, PGM 2014, Utrecht, The Ne

Description: Probabilistic Graphical Models by Linda C. van der Gaag, Ad J. Feelders This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning. Back Cover This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning. Table of Contents Structural Sensitivity for the Knowledge Engineering of Bayesian Networks.- A Pairwise Class Interaction Framework for Multilabel Classification.- From Information to Evidence in a Bayesian Network.- Learning Gated Bayesian Networks for Algorithmic Trading.- Local Sensitivity of Bayesian Networks to Multiple Simultaneous Parameter Shifts.- Bayesian Network Inference Using Marginal Trees.- On SPI-Lazy Evaluation of Influence Diagrams.- Extended Probability Trees for Probabilistic Graphical Models.- Mixture of Polynomials Probability Distributions for Grouped Sample Data.- Trading off Speed and Accuracy in Multilabel Classification.- Robustifying the Viterbi algorithm.- Extended Tree Augmented Naive Classifier.- Evaluation of Rules for Coping with Insufficient Data in Constraint-based Search Algorithms.- Supervised Classification Using Hybrid Probabilistic Decision Graphs.- Towards a Bayesian Decision Theoretic Analysis of Contextual Effect Modifiers.- Discrete Bayesian Network Interpretation of the Coxs Proportional Hazards Model.- Minimizing Relative Entropy in Hierarchical Predictive Coding.- Treewidth and the Computational Complexity of MAP Approximations.- Bayesian Networks with Function Nodes.- A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs.- Equivalences Between Maximum A Posteriori Inference in Bayesian Networks and Maximum Expected Utility Computation in Influence Diagrams.- Speeding Up $k$-Neighborhood Local Search in Limited Memory Influence Diagrams.- Inhibited Effects in CP-logic.- Learning Parameters in Canonical Models using Weighted Least Squares.- Learning Marginal AMP Chain Graphs under Faithfulness.- Learning Maximum Weighted (k+1)-order Decomposable Graphs by Integer Linear Programming.- Multi-label Classification for Tree and Directed Acyclic Graphs Hierarchies.- Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks.- Causal Discovery from Databases with Discrete and ContinuousVariables.- On Expressiveness of the AMP Chain Graph Interpretation.- Learning Bayesian Network Structures when Discrete and Continuous Variables are Present.- Learning Neighborhoods of High Confidence in Constraint-Based Causal Discovery.- Causal Independence Models for Continuous Time Bayesian Networks.- Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-Label Classification.- An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper.- Compression of Bayesian Networks with NIN-AND Tree Modeling.- A Study of Recently Discovered Equalities about Latent Tree Models using Inverse Edges.- An Extended MPL-C Model for Bayesian Network Parameter Learning with Exterior Constraints. Details ISBN3319114328 Year 2014 ISBN-10 3319114328 ISBN-13 9783319114323 Format Paperback Pages 598 Short Title PROBABILISTIC GRAPHICAL MODELS Language English Media Book Series Number 8754 Imprint Springer International Publishing AG Place of Publication Cham Country of Publication Switzerland Edited by Linda C. van der Gaag Edition 2014th Illustrations 186 Illustrations, black and white; XII, 598 p. 186 illus. Subtitle 7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014. Proceedings DOI 10.1007/978-3-319-11433-0 Author Ad J. Feelders Publisher Springer International Publishing AG Edition Description 2014 ed. Publication Date 2014-09-05 DEWEY 519.5 Audience Professional & Vocational Series Lecture Notes in Artificial Intelligence We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96291553;

Price: 131.66 AUD

Location: Melbourne

End Time: 2024-11-14T15:09:45.000Z

Shipping Cost: 126.32 AUD