Definition(Tail Events)

If \(\{X_n,n\ge 1\}\) is a sequence of random variables on \((\Omega,\mathcal{F},P)\), then the tail \(\sigma\)-field determined by \(\{X_n\}\) is given by \[ \mathcal{T}=\bigcap_{n=1}^{\infty} \sigma(X_n,X_{n+1},\ldots). \]

If \(A\in \mathcal{T}\), then \(A\) is called a tail event.



Example

\(\left\{\omega\in \Omega: \sum_{n=1}^{\infty}X_n(\omega)\mbox{ converges }\right\}\in \mathcal{T}\), because \[\begin{eqnarray*} \left\{\omega\in \Omega: \sum_{n=1}^{\infty}X_n(\omega)\mbox{ converges }\right\}=\left\{\omega\in \Omega: \sum_{n=m}^{\infty}X_n(\omega)\mbox{ converges }\right\}&\in& \sigma(X_m,X_{m+1},\ldots)\mbox{ }\forall m\ge 1\\ \implies \left\{\omega\in \Omega: \sum_{n=1}^{\infty}X_n(\omega)\mbox{ converges }\right\}\in\bigcap_{m=1}^\infty \sigma(X_m,X_{m+1},\ldots). \end{eqnarray*}\]



Theorem(Kolmogorov’s Zero-One Law)

If \(\{X_n,n\ge 1\}\) is a sequence of independent random variables and \(A\) is a tail event, then either \(P(A)=0\) or \(P(A)=1\)



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