A maximum likelihood approximation method for Dirichlet's parameter estimation

DOI10.1016/j.csda.2007.07.011zbMath1452.62211OpenAlexW2043380577MaRDI QIDQ1023456

J. Martínez

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.07.011

zbMATH Keywords

parameter estimationmaximum likelihoodDirichlet distributionproteins clustering

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10)

Related Items (5)

Partially observable Markov decision processes incorporating ⋮ On the maximization of likelihoods belonging to the exponential family using a Levenberg–Marquardt approach ⋮ Recursive parameter estimation algorithm of the Dirichlet hidden Markov model ⋮ Regression for compositions based on a generalization of the Dirichlet distribution ⋮ DIRICHLET DISTRIBUTION AND ESTIMATION OF PARAMETERS

Cites Work

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• A note on parameter estimation in the multivariate beta distribution
• A classification EM algorithm for clustering and two stochastic versions
• Small sample comparison of estimation methods for the beta distribution
• Small sample properties of parameter estimation in the dirichlet distribution
• Model-Based Gaussian and Non-Gaussian Clustering
• Maximum likelihood estimation of dirichlet distributions
• A new look at the statistical model identification

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