Approximation of probability distributions by convex mixtures of Gaussian measures

Author:

Bacharoglou Athanassia

Abstract

Let A + = { a = ( a n ) p > 1 p : a n > 0 , n N } \mathcal {A_{+}}=\{a=(a_{n})\in \bigcap _{p>1}\ell _{p}:a_{n}>0,\,\forall n\in \mathbb {N}\} and let { ϕ j } j = 1 \{\phi _{j}\}_{j=1}^{\infty } be an enumeration of all normal distributions with mean a rational number and variance 1 n 2 , n = 1 , 2 \frac {1}{n^{2}},\,n=1,2\dots . We prove that there exists an a A + a\in \mathcal {A_{+}} such that every probability density function, continuous, with compact support in R \mathbb {R} , can be approximated in L 1 L^{1} and L L^{\infty } norm simultaneously by the averages 1 j = 1 n a j j = 1 n a j ϕ j \frac {1}{\sum _{j=1}^{n}a_{j}}\,\sum _{j=1}^{n}a_{j}\phi _{j} . The set of such sequences is a dense G δ G_{\delta } set in A + \mathcal {A_{+}} and contains a dense positive cone.

Publisher

American Mathematical Society (AMS)

Subject

Applied Mathematics,General Mathematics

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