Fast MLE Computation for the Dirichlet Multinomial

2014/05/01 by Max Sklar, Sklar, Max · 1 citation
Computer Science · #Algorithms and Data Compression #Bayesian Methods and Mixture Models #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1405.0099

openalex publication_date 2014/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Given a collection of categorical data, we want to find the parameters of a Dirichlet distribution which maximizes the likelihood of that data. Newton's method is typically used for this purpose but current implementations require reading through the entire dataset on each iteration. In this paper, we propose a modification which requires only a single pass through the dataset and substantially decreases running time. Furthermore we analyze both theoretically and empirically the performance of the proposed algorithm, and provide an open source implementation.

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