Estimating Densities with Non-Parametric Exponential Families

Autor:	Yuan, Lin, Kirshner, Sergey, Givan, Robert
Rok vydání:	2012
Předmět:	Statistics - Machine Learning Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	We propose a novel approach for density estimation with exponential families for the case when the true density may not fall within the chosen family. Our approach augments the sufficient statistics with features designed to accumulate probability mass in the neighborhood of the observed points, resulting in a non-parametric model similar to kernel density estimators. We show that under mild conditions, the resulting model uses only the sufficient statistics if the density is within the chosen exponential family, and asymptotically, it approximates densities outside of the chosen exponential family. Using the proposed approach, we modify the exponential random graph model, commonly used for modeling small-size graph distributions, to address the well-known issue of model degeneracy. Comment: 22 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1206.5036 Zobrazit plný text záznamu View this record from Arxiv