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Given a collection of categorical data, we want to find the parameters of aDirichlet distribution which maximizes the likelihood of that data. Newton'smethod is typically used for this purpose but current implementations requirereading through the entire dataset on each iteration. In this paper, we proposea modification which requires only a single pass through the dataset andsubstantially decreases running time. Furthermore we analyze both theoreticallyand empirically the performance of the proposed algorithm, and provide an opensource implementation.

Fast MLE Computation for the Dirichlet Multinomial