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A Locally Adaptive Normal Distribution

Georgios Arvanitidis, Lars K. Hansen, Søren Hauberg

Neural Information Processing Systems

The underlyingmetricis,however,non-parametric.Wedevelopamaximumlikelihood algorithm to infer the distribution parameters that relies on a combination of gradient descent and Monte Carlo integration. We further extend the LAND to mixture models, andprovidethecorresponding EMalgorithm.