Daniel Suess

Daniel Suessdaniel-suess

Dec 08 2017 10:28 UTC
Dec 08 2017 10:27 UTC
Dec 08 2017 10:26 UTC
Dec 08 2017 10:26 UTC
Dec 07 2017 13:22 UTC
Dec 07 2017 12:49 UTC
Dec 06 2017 11:29 UTC
Dec 05 2017 12:30 UTC
Dec 04 2017 15:53 UTC
Nov 30 2017 02:00 UTC
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.
Nov 09 2017 14:56 UTC
Nov 09 2017 14:54 UTC
Nov 09 2017 14:53 UTC
Nov 07 2017 16:08 UTC
Nov 07 2017 16:07 UTC
Oct 30 2017 11:38 UTC
Daniel Suess scited Energy Clustering
Oct 30 2017 11:37 UTC