Probabilistic Graphical Models
Springer International Publishing (Verlag)
978-3-319-11432-3 (ISBN)
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 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.
| Erscheint lt. Verlag | 5.9.2014 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XII, 598 p. 186 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 926 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Artificial Intelligence • Bayesian networks • Belief Networks • classification • Data Mining • decision networks • Graph Algorithms • graph theory • Influence Diagrams • learning in probabilistic graphical models • machine learning • probabilistic representations • probability and statistics • Search Methods • Trees |
| ISBN-10 | 3-319-11432-8 / 3319114328 |
| ISBN-13 | 978-3-319-11432-3 / 9783319114323 |
| Zustand | Neuware |
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