Randomized Algorithms (Spring 2010)/Martingales

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Martingales

Review of conditional probability

Martingales and Azuma's Inequality

Azuma's Inequality:
Let [math]\displaystyle{ X_0,X_1,\ldots }[/math] be a martingale such that, for all [math]\displaystyle{ k\ge 1 }[/math],
[math]\displaystyle{ |X_{k}-X_{k-1}|\le c_k, }[/math]

Then

[math]\displaystyle{ \begin{align} \Pr\left[|X_n-X_0|\ge t\right]\le 2\exp\left(-\frac{t^2}{2\sum_{k=1}^nc_k^2}\right). \end{align} }[/math]


Corollary:
Let [math]\displaystyle{ X_0,X_1,\ldots }[/math] be a martingale such that, for all [math]\displaystyle{ k\ge 1 }[/math],
[math]\displaystyle{ |X_{k}-X_{k-1}|\le c, }[/math]

Then

[math]\displaystyle{ \begin{align} \Pr\left[|X_n-X_0|\ge ct\sqrt{n}\right]\le 2 e^{-t^2/2}. \end{align} }[/math]

Generalizations

Azuma's Inequality (general version):
Let [math]\displaystyle{ Y_0,Y_1,\ldots }[/math] be a martingale with respect to the sequence [math]\displaystyle{ X_0,X_1,\ldots }[/math] such that, for all [math]\displaystyle{ k\ge 1 }[/math],
[math]\displaystyle{ |Y_{k}-Y_{k-1}|\le c_k, }[/math]

Then

[math]\displaystyle{ \begin{align} \Pr\left[|Y_n-Y_0|\ge t\right]\le 2\exp\left(-\frac{t^2}{2\sum_{k=1}^nc_k^2}\right). \end{align} }[/math]

The Method of Bounded Differences

Applications