Geometric Inference for Probability Measures

  title={Geometric Inference for Probability Measures},
  author={Fr{\'e}d{\'e}ric Chazal and David Cohen-Steiner and Quentin M{\'e}rigot},
  journal={Foundations of Computational Mathematics},
Data often comes in the form of a point cloud sampled from an unknown compact subset of Euclidean space. The general goal of geometric inference is then to recover geometric and topological features (eg. Betti numbers, normals) of this subset from the approximating point cloud data. In recent years, it appeared that the study of distance functions allows to address many of these questions successfully. However, one of the main limitations of this framework is that it does not cope well with… CONTINUE READING
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