| 31. | This multinomial expansion is also, of course, what essentially underlies the binomial theorem-based proof above)
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| 32. | The parameter \ alpha _ { 0 } governs the degree of overdispersion or burstiness relative to the multinomial.
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| 33. | Shortly after publishing this paper, de Moivre also generalized Newton's noteworthy binomial theorem into the multinomial theorem.
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| 34. | A generalisation of the logistic function to multiple inputs is the softmax activation function, used in multinomial logistic regression.
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| 35. | It is possible to " read off " the multinomial coefficients from the terms by using the multinomial coefficient formula.
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| 36. | It is possible to " read off " the multinomial coefficients from the terms by using the multinomial coefficient formula.
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| 37. | It can also be shown that it approaches the multinomial distribution as \ alpha _ { 0 } approaches infinity.
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| 38. | In fact, the principle involved here is described in fair detail in the article on the Dirichlet-multinomial distribution.
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| 39. | For multiple underlyers multinomial lattices can be built, although the number of nodes increases exponentially with the number of underlyers.
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| 40. | If the dependent variable is discrete, some of those superior methods are logistic regression, multinomial logit and probit models.
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