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ORCIDAMBN@JSAI-isAI 2015: Yokohama, Japan
- Joe Suzuki, Maomi Ueno:
Advanced Methodologies for Bayesian Networks - Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings. Lecture Notes in Computer Science 9505, Springer 2015, ISBN 978-3-319-28378-4 - Joe Suzuki:
Efficiently Learning Bayesian Network Structures Based on the B&B Strategy: A Theoretical Analysis. 1-14 - Kazuki Natori, Masaki Uto, Yu Nishiyama, Shuichi Kawano
, Maomi Ueno:
Constraint-Based Learning Bayesian Networks Using Bayes Factor. 15-31 - Zhi-gao Guo, Xiao-Guang Gao, Ruo-hai Di, Yu Yang:
Learning Bayesian Network Parameters from Small Data Set: A Spatially Maximum a Posteriori Method. 32-45 - Niklas Jahnsson, Brandon M. Malone, Petri Myllymäki:
Hashing-Based Hybrid Duplicate Detection for Bayesian Network Structure Learning. 46-60 - Yi Zuo, Katsutoshi Yada, Eisuke Kita:
A Bayesian Network Approach for Predicting Purchase Behavior via Direct Observation of In-store Behavior. 61-75 - Alessio Benavoli, Cassio P. de Campos:
Statistical Tests for Joint Analysis of Performance Measures. 76-92 - Yun Zhou, John Howroyd, Sebastian Danicic, J. Mark Bishop:
Extending Naive Bayes Classifier with Hierarchy Feature Level Information for Record Linkage. 93-104 - Brandon Malone:
Empirical Behavior of Bayesian Network Structure Learning Algorithms. 105-121 - Yuan Zou, Teemu Roos:
On Model Selection, Bayesian Networks, and the Fisher Information Integral. 122-135 - Jingguo Dai, Jia Ren:
Unsupervised Evolutionary Algorithm for Dynamic Bayesian Network Structure Learning. 136-151 - Chao Li, Maomi Ueno:
A Fast Clique Maintenance Algorithm for Optimal Triangulation of Bayesian Networks. 152-167 - Shan Gao, Shin-ichi Minato:
Factorization of ZDDs for Representing Bayesian Networks Based on d-Separations. 168-183 - Karthika Mohan, Judea Pearl:
Missing Data from a Causal Perspective. 184-195 - Takashi Isozaki, Manabu Kuroki:
Learning Maximal Ancestral Graphs with Robustness for Faithfulness Violations. 196-208 - Patrick Blöbaum, Shohei Shimizu, Takashi Washio:
Discriminative and Generative Models in Causal and Anticausal Settings. 209-221 - Shohei Shimizu:
A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes. 222-233 - Joe Suzuki:
Forest Learning Based on the Chow-Liu Algorithm and Its Application to Genome Differential Analysis: A Novel Mutual Information Estimation. 234-249 - Russell G. Almond:
Tips and Tricks for Building Bayesian Networks for Scoring Game-Based Assessments. 250-263
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