000 05451nam a22006015i 4500
001 978-3-319-11433-0
003 DE-He213
005 20240423125556.0
007 cr nn 008mamaa
008 140911s2014 sz | s |||| 0|eng d
020 _a9783319114330
_9978-3-319-11433-0
024 7 _a10.1007/978-3-319-11433-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aProbabilistic Graphical Models
_h[electronic resource] :
_b7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014. Proceedings /
_cedited by Linda C. van der Gaag, Ad J. Feelders.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXII, 598 p. 186 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v8754
505 0 _aStructural 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 Cox's 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.
520 _aThis 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.
650 0 _aArtificial intelligence.
650 0 _aComputer science
_xMathematics.
650 0 _aMathematical statistics.
650 0 _aData mining.
650 0 _aDiscrete mathematics.
650 1 4 _aArtificial Intelligence.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aDiscrete Mathematics in Computer Science.
700 1 _avan der Gaag, Linda C.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aFeelders, Ad J.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319114323
776 0 8 _iPrinted edition:
_z9783319114347
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v8754
856 4 0 _uhttps://doi.org/10.1007/978-3-319-11433-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cSPRINGER
999 _c179456
_d179456