Approximate Inference in Collective Graphical Models

  title={Approximate Inference in Collective Graphical Models},
  author={Daniel Sheldon and Tao Sun and Akshat Kumar and Thomas G. Dietterich},
We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical models, exact inference in CGMs is NP-hard even for tree-structured models. We then develop a tractable convex approximation to the NPhard MAP inference problem in CGMs, and show how to use MAP inference… CONTINUE READING
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