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This document summarizes an algorithm for direct and inverse inference on large music metadata databases. Direct inference infers missing attribute values given some observed attributes, while inverse inference identifies minimal changes to observed attributes to match target attribute values. The algorithm is based on kernel regression trained on a large database of music metadata. It provides more computationally tractable inference than Bayesian networks on this data. The authors evaluate the direct infe

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