A new lazy-update algorithm claims near-linear O(ε^{-1}nd log(n/d)) time for approximate John ellipsoids, but key proof steps and complexity accounting contain gaps.
On products of Gaussian random variables
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abstract
Sums of independent random variables form the basis of many fundamental theorems in probability theory and statistics, and therefore, are well understood. The related problem of characterizing products of independent random variables seems to be much more challenging. In this work, we investigate such products of normal random variables, products of their absolute values, and products of their squares. We compute power-log series expansions of their cumulative distribution function (CDF) based on the theory of Fox H-functions. Numerically we show that for small arguments the CDFs are well approximated by the lowest orders of this expansion. For the two non-negative random variables, we also compute the moment generating functions in terms of Meijer G-functions, and consequently, obtain a Chernoff bound for sums of such random variables.
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John Ellipsoids via Lazy Updates
A new lazy-update algorithm claims near-linear O(ε^{-1}nd log(n/d)) time for approximate John ellipsoids, but key proof steps and complexity accounting contain gaps.