Generalized Beta Divergence

Yilmaz, Y. Kenan

arXiv.org Machine Learning 

Divergences and distributions are deeply related concepts studied extensively in various fields. This paper is another attempt that casts their relations specifically into that of beta divergences and dispersion models and studies accordingly. The main consequence of this study is that beta divergence and (half of) statistical deviance are represented identical equations and they are, therefore, equivalent measures. In this respect, formulation of beta divergence is generalized and thus is extended beyond its Tweedie related classical forms [1], [2], [3], [4] and is aligned with exponential dispersion models. This is achieved by defining beta divergences as a function of so-called variance functions of exponential dispersion models. One immediate consequence is that we can compute beta divergence for non-Tweedie models such as negative binomial or hyperbolic secant distribution.

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