Probability based independence sampler for Bayesian quantitative learning in graphical log-linear marginal models

DOI10.1214/18-BA1128zbMath1421.62029arXiv1807.01152MaRDI QIDQ2316984

Monia Lupparelli, Ioannis Ntzoufras, Claudia Tarantola

Publication date: 7 August 2019

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.01152

zbMATH Keywords

graphical modelsmarginal log-linear parameterisationMarkov chain Monte Carlo computation

Mathematics Subject Classification ID

Bayesian inference (62F15) Exact distribution theory in statistics (62E15) Learning and adaptive systems in artificial intelligence (68T05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (3)

Context-specific independencies in hierarchical multinomial marginal models ⋮ Probability based independence sampler for Bayesian quantitative learning in graphical log-linear marginal models ⋮ Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

Uses Software

• CODA
• boa
• RStan
• rstan

Cites Work

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• Maximum-Likelihood Estimation of Parameters Subject to Restraints
• Binary Models for Marginal Independence
• Parameterizations and Fitting of Bi‐directed Graph Models to Categorical Data
• Markov Properties for Acyclic Directed Mixed Graphs
• Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models
• Marginal Log-Linear Parameters for Graphical Markov Models

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