We propose an efficient method for Bayesian network inference in models with functional dependence. We generalize the multiplicative factorization method originally designed by Takikawa and D Ambrosio(1999) FOR models WITH independence OF causal influence.Using a hidden variable, we transform a probability potential INTO a product OF two - dimensional potentials.The multiplicative factorization yields more efficient inference. FOR example, IN junction tree propagation it helps TO avoid large cliques. IN ORDER TO keep potentials small, the number OF states OF the hidden variable should be minimized.We transform this problem INTO a combinatorial problem OF minimal base IN a particular space.We present an example OF a computerized adaptive test, IN which the factorization method IS significantly more efficient than previous inference methods.
A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions
An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey
The University of Texas at El Paso（德克萨斯大学埃尔帕索分校）；Office of Naval Research（海军研究办公室）；Palo Alto Networks Inc（帕洛阿尔托网络公司）
Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation
Institute for Intelligent Cooperating Systems, Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学智能协作系统研究所）；Smead Aerospace Engineering Sciences Department, University of Colorado Boulder（科罗拉多大学博尔德分校斯米德航空航天工程科学系）
Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization
Vector Institute（向量研究所）；INRIA Saclay（法国国家信息与自动化研究所萨克雷研究中心）；University of Waterloo（滑铁卢大学）；Waterloo Institute for Nanotechnology（滑铁卢纳米技术研究所）；Western University（西安大略大学）
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Bayesian Optimization for Function-Valued Responses under Min-Max Criteria
School of Industrial and Systems Engineering（工业与系统工程学院）；Georgia Institute of Technology（佐治亚理工学院）；School of Electrical and Computer Engineering（电气与计算机工程学院）
