  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.
