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A conditional probability distribution of a random variable, to be contrasted with its unconditional or a priori distribution.
Let <html> be a random parameter with an a priori density , let be a random result of observations and let be the conditional density of when ; then the a posteriori distribution of for a given </html>, according to the Bayes formula, has the density
If <html></html> is a sufficient statistic for the family of distributions with densities <html>, then the a posteriori distribution depends not on itself, but on . The asymptotic behaviour of the a posteriori distribution as , where are the results of independent observations with density ,</html> is "almost independent" of the a priori distribution of <html></html>.
For the role played by a posteriori distributions
in the theory of statistical decisions, see
Bayesian approach.
References
<html>
[1] |
S.N. Bernshtein, "Probability theory" , Moscow-Leningrad (1946) (In Russian) |
</html>
Yu.V. Prokhorov
Comments
References
<html>
[a1] |
E. Sverdrup, "Laws and chance variations" , 1 , North-Holland (1967) pp. 214ff |
</html>
This text originally appeared in Encyclopaedia of Mathematics
- ISBN 1402006098
Sidebar. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Sidebar&oldid=3006