Randomized Algorithms (Spring 2010)/Martingales: Difference between revisions

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{|border="1"
{|border="1"
|'''Theorem (The method of averaged bounded differences):'''  
|'''Theorem (The method of averaged bounded differences):'''  
:Let <math>X_0,X_1,\ldots, X_n</math> be an arbitrary set of random variables and let <math>f</math> be a function of <math>X_0,X_1,\ldots, X_n</math> satisfying that, for all <math>1\le i\le n</math>,
:Let <math>X=(X_0,X_1,\ldots, X_n)</math> be arbitrary random variables and let <math>f</math> be a function of <math>X_0,X_1,\ldots, X_n</math> satisfying that, for all <math>1\le i\le n</math>,
::<math>
::<math>
|\mathbf{E}[f\mid X_1,\ldots,X_i]-\mathbf{E}[f\mid X_1,\ldots,X_{i-1}]|\le c_i,
|\mathbf{E}[f(X)\mid X_1,\ldots,X_i]-\mathbf{E}[f(X)\mid X_1,\ldots,X_{i-1}]|\le c_i,
</math>
</math>
:Then
:Then
::<math>\begin{align}
::<math>\begin{align}
\Pr\left[|f-\mathbf{E}[f]|\ge t\right]\le 2\exp\left(-\frac{t^2}{2\sum_{i=1}^nc_i^2}\right).
\Pr\left[|f(X)-\mathbf{E}[f(X)]|\ge t\right]\le 2\exp\left(-\frac{t^2}{2\sum_{i=1}^nc_i^2}\right).
\end{align}</math>
\end{align}</math>
|}
|}
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{|border="1"
{|border="1"
|'''Method of bounded differences:'''  
|'''Method of bounded differences:'''  
:Let <math>X_0,X_1,\ldots, X_n</math> be independent random variables and let <math>f</math> be a function satisfying the Lipschitz condition.
:Let <math>X=(X_0,X_1,\ldots, X_n)</math> be independent random variables and let <math>f</math> be a function satisfying the Lipschitz condition.
:Then
:Then
::<math>\begin{align}
::<math>\begin{align}
\Pr\left[|f-\mathbf{E}[f]|\ge t\right]\le 2\exp\left(-\frac{t^2}{2n}\right).
\Pr\left[|f(X)-\mathbf{E}[f(X)]|\ge t\right]\le 2\exp\left(-\frac{t^2}{2n}\right).
\end{align}</math>
\end{align}</math>
|}
|}


== Applications ==
== Applications ==

Revision as of 13:10, 6 April 2010

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

Theorem (The method of averaged bounded differences):
Let [math]\displaystyle{ X=(X_0,X_1,\ldots, X_n) }[/math] be arbitrary random variables and let [math]\displaystyle{ f }[/math] be a function of [math]\displaystyle{ X_0,X_1,\ldots, X_n }[/math] satisfying that, for all [math]\displaystyle{ 1\le i\le n }[/math],
[math]\displaystyle{ |\mathbf{E}[f(X)\mid X_1,\ldots,X_i]-\mathbf{E}[f(X)\mid X_1,\ldots,X_{i-1}]|\le c_i, }[/math]
Then
[math]\displaystyle{ \begin{align} \Pr\left[|f(X)-\mathbf{E}[f(X)]|\ge t\right]\le 2\exp\left(-\frac{t^2}{2\sum_{i=1}^nc_i^2}\right). \end{align} }[/math]


Method of bounded differences:
Let [math]\displaystyle{ X=(X_0,X_1,\ldots, X_n) }[/math] be independent random variables and let [math]\displaystyle{ f }[/math] be a function satisfying the Lipschitz condition.
Then
[math]\displaystyle{ \begin{align} \Pr\left[|f(X)-\mathbf{E}[f(X)]|\ge t\right]\le 2\exp\left(-\frac{t^2}{2n}\right). \end{align} }[/math]

Applications