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diff --git a/paper/sections/results.tex b/paper/sections/results.tex index f68ecee..7eb3973 100644 --- a/paper/sections/results.tex +++ b/paper/sections/results.tex @@ -273,7 +273,7 @@ condition on the \emph{gram matrix} of the observations $X^T X = \paragraph{(RE) with high probability} The Generalized Linear Cascade model yields a probability distribution over the -observed sets of infeceted nodes $(x^t)_{t\in\mathcal{T}}$. It is then natural +observed sets of infected nodes $(x^t)_{t\in\mathcal{T}}$. It is then natural to ask whether the restricted eigenvalue condition is likely to occur under this probabilistic model. Several recent papers show that large classes of correlated designs obey the restricted eigenvalue property with high |