  Incorporating domain knowledge into the modeling process is an effective way
to improve learning accuracy. However, as it is provided by humans, domain
knowledge can only be specified with some degree of uncertainty. We propose to
explicitly model such uncertainty through probabilistic constraints over the
parameter space. In contrast to hard parameter constraints, our approach is
effective also when the domain knowledge is inaccurate and generally results in
superior modeling accuracy. We focus on generative and conditional modeling
where the parameters are assigned a Dirichlet or Gaussian prior and demonstrate
the framework with experiments on both synthetic and real-world data.
