Randomized Algorithms (Spring 2010)/Martingales: Difference between revisions
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== The Method of Bounded Differences == | == The Method of Bounded Differences == | ||
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|'''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> | |||
|} | |||
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|'''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> | |||
|} | |||
== Applications == | == Applications == |
Revision as of 13:09, 6 April 2010
Martingales
Review of conditional probability
Martingales and Azuma's Inequality
Azuma's Inequality:
Then
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Corollary:
Then
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Generalizations
Azuma's Inequality (general version):
Then
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The Method of Bounded Differences
Theorem (The method of averaged bounded differences):
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Method of bounded differences:
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