Markov chain Monte Carlo methods for probabilistic network model determination

DOI10.1007/BF03178927zbMath1454.62019MaRDI QIDQ5123753

Paolo Giudici

Publication date: 29 September 2020

Published in: Journal of the Italian Statistical Society (Search for Journal in Brave)

zbMATH Keywords

latent variablesBayesian model selectionMarkov chain Monte Carlo methodsgraphical modelsprobabilistic neural networks

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Probabilistic graphical models (62H22)

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Hyper Markov laws in the statistical analysis of decomposable graphical models
• Markov chains for exploring posterior distributions. (With discussion)
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• Miscellanea. Identification of a single-factor model using graphical Gaussian rules
• Decomposable graphical Gaussian model determination
• Miscellanea. On the identification of a single-factor model with correlated residuals