Difference between revisions of "Dirichlet"
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The Dirichlet distribution has a density given by | The Dirichlet distribution has a density given by | ||
− | k * Product( X^(alpha-1), I) | + | k * [[Product]]( X^(alpha-1), I) |
where k is a normalization factor equal to | where k is a normalization factor equal to | ||
− | GammaFn( | + | [[GammaFn]]( [[Sum]](alpha,I )) / [[Sum]]([[GammaFn]](alpha),I) |
The parameters, alpha, can be interpreted as observation counts. The mean is given by the relative values of alpha (normalized to 1), but the variance narrows as the alphas get larger, just as your confidence in a distribution would narrow as you get more samples. | The parameters, alpha, can be interpreted as observation counts. The mean is given by the relative values of alpha (normalized to 1), but the variance narrows as the alphas get larger, just as your confidence in a distribution would narrow as you get more samples. |
Revision as of 16:52, 20 April 2007
Dirichlet( alpha,I )
A Dirichlet distribution with parameters alpha_i>0.
Each sample of a Dirichlet distribution produces a random vector whose elements sum to 1. It is commonly used to represent second order probability information.
The Dirichlet distribution has a density given by
k * Product( X^(alpha-1), I)
where k is a normalization factor equal to
GammaFn( Sum(alpha,I )) / Sum(GammaFn(alpha),I)
The parameters, alpha, can be interpreted as observation counts. The mean is given by the relative values of alpha (normalized to 1), but the variance narrows as the alphas get larger, just as your confidence in a distribution would narrow as you get more samples.
The Dirichlet lends itself to easy Bayesian updating. If you have a prior of alpha0, and you observe N
Library
Multivariate Distributions.ana
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