2016/05/20 by Yusheng Xie, Nan Du, Xie, Yusheng +7
Computer Science · Mathematics · #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian network #Bayesian probability #Computation #Computer science #Data mining #FOS: Computer and information sciences #Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Machine learning #Mathematical optimization #Mathematics #Prior probability #Ranking (information retrieval) #Scalability #Transformation (genetics) #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1605.06181
arxiv created 2016/05/20 · openalex publication_date 2016/05/20 · arxiv updated 2016/05/23 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Variational inference provides approximations to the computationally intractable posterior distribution in Bayesian networks. A prominent medical application of noisy-or Bayesian network is to infer potential diseases given observed symptoms. Previous studies focus on approximating a handful of complicated pathological cases using variational transformation. Our goal is to use variational transformation as part of a novel hybridized inference for serving reliable and real time diagnosis at web scale. We propose a hybridized inference that allows variational parameters to be estimated without disease posteriors or priors, making the inference faster and much of its computation recyclable. In addition, we propose a transformation ranking algorithm that is very stable to large variances in network prior probabilities, a common issue that arises in medical applications of Bayesian networks. In experiments, we perform comparative study on a large real life medical network and scalability study on a much larger (36,000x) synthesized network.