Software modules categorization through likelihood and bayesian analysis of finite dirichlet mixtures

DOI10.1080/02664760802684185OpenAlexW2068340419MaRDI QIDQ5123513

Nizar Bouguila, A. Ben Hamza, Jian Han Wang

Publication date: 29 September 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664760802684185

zbMATH Keywords

Bayesian analysismaximum likelihoodDirichlet distributionGibbs samplingMetropolis-Hastingsmixture modelingsoftware modules

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Bayesian Inference of Hidden Markov Models Using Dirichlet Mixtures

Uses Software

• Gibbsit

Cites Work

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• A characterisation of Dirichlet distributions
• Modeling by shortest data description
• A new characterization of the Dirichlet distribution through neutrality
• A note on parameter estimation in the multivariate beta distribution
• Estimating the dimension of a model
• A formula on multivariate Dirichlet distributions
• A transient random walk on stochastic matrices with Dirichlet distributions
• A characterization of the Dirichlet distribution through global and local parameter independence
• Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables
• Inference from iterative simulation using multiple sequences
• Two characterizations of the Dirichlet distribution
• Sampling-Based Approaches to Calculating Marginal Densities
• The Calculation of Posterior Distributions by Data Augmentation
• A Complexity Measure
• Perfect Samplers for Mixtures of Distributions
• Applied Bayesian Modelling
• Bayes Factors
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• A Characterization of the Dirichlet Distribution
• Maximum likelihood estimation of dirichlet distributions
• The Monte Carlo Method

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