Reimar Hofmann

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In a Bayesian framework, we give a principled account of how domain-specific prior knowledge such as imperfect analytic domain theories can be optimally incorporated into networks of locally-tuned units: by choosing a specific architecture and by applying a specific training regimen. Our method proved successful in overcoming the data deficiency problem in(More)
We present a systematic approach to mean-field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean-field equations derived here may serve as a local, and thus very simple, method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. Our approach is 'model-independent' in(More)
We derive solutions for the problem of missing and noisy data in nonlinear time-series prediction from a probabilistic point of view. We discuss diierent approximations to the solutions, in particular approximations which require either stochastic simulation or the substitution of a single estimate for the missing data. We show experimentally that commonly(More)
We present a systematic, model-independent formulation of mean eld theory (MFT) as an inference method in probabilistic models. \Model-independent" means that we do not assume a particular type of dependency among the variables of a domain but instead work in a general probabilistic setting. In a Bayesian network, for example, you may use arbitrary tables(More)
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