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| == The Method of Bounded Differences == | | == The Method of Bounded Differences == |
| | {|border="1" |
| | |'''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>, |
| | ::<math> |
| | |\mathbf{E}[f\mid X_1,\ldots,X_i]-\mathbf{E}[f\mid X_1,\ldots,X_{i-1}]|\le c_i, |
| | </math> |
| | :Then |
| | ::<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). |
| | \end{align}</math> |
| | |} |
| | |
| | |
| | {|border="1" |
| | |'''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. |
| | :Then |
| | ::<math>\begin{align} |
| | \Pr\left[|f-\mathbf{E}[f]|\ge t\right]\le 2\exp\left(-\frac{t^2}{2n}\right). |
| | \end{align}</math> |
| | |} |
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| == Applications == | | == Applications == |
Revision as of 13:09, 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_0,X_1,\ldots, X_n }[/math] be an arbitrary set of 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\mid X_1,\ldots,X_i]-\mathbf{E}[f\mid X_1,\ldots,X_{i-1}]|\le c_i,
}[/math]
- Then
- [math]\displaystyle{ \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).
\end{align} }[/math]
|
Method of bounded differences:
- Let [math]\displaystyle{ 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-\mathbf{E}[f]|\ge t\right]\le 2\exp\left(-\frac{t^2}{2n}\right).
\end{align} }[/math]
|
Applications