Monte Carlo methods for Bayesian analysis of constrained parameter problems

DOI10.1093/biomet/85.1.73zbMath0904.62035OpenAlexW2073541772MaRDI QIDQ3842825

Ming-Hui Chen, Qui-Man Shao

Publication date: 1998

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/77c99a270c7a00239647960babcdb312117532a8

zbMATH Keywords

Gibbs samplingBayesian computationBayesian hierarchical modeljob satisfactionmarginal posterior density estimationmeal, ready to-eat model

Mathematics Subject Classification ID

Bayesian inference (62F15) Probabilistic methods, stochastic differential equations (65C99)

Related Items (9)

Bayesian object classification of gold nanoparticles ⋮ Minkowski–Weyl Priors for Models With Parameter Constraints: An Analysis of the BioCycle Study ⋮ Bayesian Inferences on Predictors of Conception Probabilities ⋮ A Class of Bayesian Shared Gamma Frailty Models with Multivariate Failure Time Data ⋮ Problematic effects of aggregation in \(z\)ROC analysis and a hierarchical modeling solution ⋮ Bayesian Inference on Order-Constrained Parameters in Generalized Linear Models ⋮ A Bayesian approach to inequality constrained linear mixed models: estimation and model selection ⋮ Cure rate models: A unified approach ⋮ Bayesian variable selection for proportional hazards models

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