From ec61b8b5528045dd7a1fe82939d1911c11e74526 2017-09-08 14:32:50 From: Tom Bannink Date: 2017-09-08 14:32:50 Subject: [PATCH] Add event defs --- diff --git a/main.tex b/main.tex index 229303ac5f091f14e934a338256902a48279a050..45372e8fec3b5870badfa21c2e09674719d91a34 100644 --- a/main.tex +++ b/main.tex @@ -61,6 +61,7 @@ \newcommand{\diam}[1]{\mathcal{D}\left(#1\right)} \newcommand{\paths}[1]{\mathcal{P}\left(#1\to\mathbf{1}\right)} \newcommand{\start}[1]{\textsc{start}\left(#1\right)} +\newcommand{\initone}[1]{\textsc{InitOne}\left(#1\right)} \newcommand{\maxgap}[1]{\mathrm{maxgap}\left(#1\right)} \newcommand{\gaps}[1]{#1_{\mathrm{gaps}}} \renewcommand{\P}{\mathbb{P}} @@ -593,14 +594,13 @@ We consider the following generalization of the Markov Chain. Let $G=(V,E)$ be a graph with vertex set $V$ and edge set $E$. We define a Markov Chain $\mathcal{M}_G$ as the following process: initialize every vertex of $G$ independently to 0 with probability $p$ and 1 with probability $1-p$. Then at each step, select a uniformly random vertex that has value $0$ and resample it and its neighbourhood, all of them independently with the same probability $p$. The Markov Chain terminates when all vertices have value $1$. We use $\P^{G}$ to denote probabilities associated to this Markov Chain. \begin{definition}[Events] \label{def:events} - For any subset $S\subseteq V$ of vertices. - For any state $b\in\{0,1\}^n$, define $\start{b}$ as the event that the starting state of the chain is the state $b$. For any event $A$ and any $v\in[n]$, define + Let $S\subseteq V$ be any subset of vertices. + Define $\Z{S}$ as the event that \emph{all} vertices in $S$ become zero at any point in time before the Markov Chain terminates. + Define $\NZ{S}$ as the event that \emph{none} of the vertices in $S$ become zero at any point in time before the Markov Chain terminates. + Define $\initone{S}$ as the event that all vertices in $S$ \emph{initially} get assigned the value 1, and define for any event $A$: \begin{align*} - \P^{(n)}_b(A) &= \P^{(n)}(A \;|\; \start{b}) \\ - \P^{[n]}_{b_v=1}(A) &= \P^{[n]}(A \;|\; v\text{ is initialized to }1) \\ - \P^{[n]}_{b_v=b_w=1}(A) &= \P^{[n]}(A \;|\; v\text{ and }w\text{ are initialized to }1) , + \P^{G}_S(A) &= \P^{G}(A \;\mid\; \initone{S}) \end{align*} - The last two probabilities are not conditioned on any other bits of the starting state. \end{definition}