Variational hybridization and transformation for large inaccurate noisy-or networks
Abstract
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.
Keywords
Cite
@article{arxiv.1605.06181,
title = {Variational hybridization and transformation for large inaccurate noisy-or networks},
author = {Yusheng Xie and Nan Du and Wei Fan and Jing Zhai and Weicheng Zhu},
journal= {arXiv preprint arXiv:1605.06181},
year = {2016}
}