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