A Generalized Multinomial Distribution from Dependent Categorical Random Variables

A Generalized Multinomial Distribution from Dependent Categorical Random Variables

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Conclusion

Categorical variables play a large role in many statistical and practical applications across disciplines. Moreover, correlations among categorical variables are common and found in many scenarios, which can cause problems with conventional assumptions. Different approaches have been taken to mitigate these effects, because a mathematical framework to define a measure of dependency in a sequence of categorical variables was not available. This paper formalized the notion of dependent categorical variables under a first-dependence scheme and proved that such a sequence is identically distributed but now dependent. With an identically distributed but dependent sequence, a generalized multinomial distribution was derived in Section~\ref{sec: gen multinomial} and important properties of this distribution were provided. An efficient algorithm to generate a sequence of dependent categorical random variables was given.
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