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Languages for Open-Universe Probabilistic Models (OUPMs) can represent situations with an unknown number of objects and identity uncertainty. While such cases arise in a wide range of important real-world applications, existing general purpose inference methods for OUPMs are far less efficient than those available for more restricted languages and model classes. This paper goes some way to remedying this de cit by introducing, and proving correct, a generalization of Gibbs sampling to partial worlds with possibly varying model structure.
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