Hypergeometric Distribution: Difference between revisions
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\begin{align} | \begin{align} | ||
K &=& \mbox{Number of successful trials} \\ | K &=& \mbox{Number of successful trials} \\ | ||
N &=& \mbox{ | N &=& \mbox{Population size} \\ | ||
k &=& \mbox{Number of targets for trials} \\ | k &=& \mbox{Number of targets for trials} \\ | ||
n &=& \mbox{Sample size} | n &=& \mbox{Sample size} | ||
Revision as of 21:31, 9 March 2019
Hypergeometric distribution counts the number of ways you can obtain particular target values when sampling from a population without replacement.
This describes many systems, most notably a deck of 52 Cards and dealing e.g. poker hands.
Hypergeometric distribution:
$ \dfrac{ \binom{K}{k} \binom{N-K}{n-k} }{ \binom{N}{n} } $
wehere:
$ \begin{align} K &=& \mbox{Number of successful trials} \\ N &=& \mbox{Population size} \\ k &=& \mbox{Number of targets for trials} \\ n &=& \mbox{Sample size} \end{align} $
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Combinatorics
Combinatorial Structures - Order Does Not Matter Ordinary generating functions
Labelled Structures - Order Matters Enumerating Permutations: String Permutations Generating Permutations: Cool · Algorithm M (add-one) · Algorithm G (Gray binary code)
Combinatorics Problems Longest Increasing Subsequence · Maximum Value Contiguous Subsequence · Racing Gems Cards (poker hands with a deck of 52 playing cards)
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