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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Statistical analysis of the estimation of distance measures
Mark Pinsky1, Dina Yogev-Einot, David Avnir
1Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel. mark@dina.es.huji.ac.il
Abstract:
Distance functions serve for quantitative evaluation of the degree of similarity, shape content, symmetry, chirality, and so on. We have developed a general methodology and a general computational tool for the estimation of the value of the distance function and of the error in that estimation, which originates in the experimental uncertainty in the location of the set of points of the studied structure (such as that expressed by the atomic displacement factor in X-ray data analysis).
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