Difference between revisions of "Expectation (math)"

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The mathematical '''expectation''' of a continuously distributed variable <math>X</math> with [[probability density function]]
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The mathematical '''expectation''' of a continuously distributed random variable <math>X</math> with [[probability density function]]
 
<math>f(x)</math> is
 
<math>f(x)</math> is
 
:<math>
 
:<math>
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f(x)dx.
 
f(x)dx.
 
</math>
 
</math>
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The expectation is also the [[mean]] of a distributed variable <math>X</math>.
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The expectation is also the [[mean]] <math>X</math>.
  
 
The expectation with respect to some function <math>g(X)</math> where <math>X</math> is distributed according to <math>f(x)</math> is
 
The expectation with respect to some function <math>g(X)</math> where <math>X</math> is distributed according to <math>f(x)</math> is
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</math>
 
</math>
  
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For a discretely distributed variable <math>X</math> with [[probability mass function]]
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For a discretely distributed random variable <math>X</math> with [[probability mass function]]
 
<math>p_{k}</math> it is
 
<math>p_{k}</math> it is
 
:<math>
 
:<math>

Revision as of 18:02, February 22, 2009

The mathematical expectation of a continuously distributed random variable <math>X</math> with probability density function <math>f(x)</math> is

<math>

\mbox{E}[X] =\int\limits_{-\infty}^\infty x f(x)dx. </math> The expectation is also the mean <math>X</math>.

The expectation with respect to some function <math>g(X)</math> where <math>X</math> is distributed according to <math>f(x)</math> is

<math>

\mbox{E}[g(X)] =\int\limits_{-\infty}^\infty g(x) f(x)dx. </math>

For a discretely distributed random variable <math>X</math> with probability mass function <math>p_{k}</math> it is

<math>

\mbox{E}[X]=\sum_{k} p_{k}x_{k}. </math>