A Bayesian model selection method with applications

DOI10.1016/S0167-9473(02)00073-7zbMath0993.62026MaRDI QIDQ1614838

Xin-Yuan Song, Sik-Yum Lee

Publication date: 9 September 2002

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

zbMATH Keywords

path samplingBayes factorGibbs samplermixture modelslatent variable modelsposterior simulation

Mathematics Subject Classification ID

Related Items (7)

A path sampling identity for computing the Kullback-Leibler and J divergences ⋮ New approaches to compute Bayes factor in finite mixture models ⋮ Posterior analysis of latent competing risk models by parallel tempering ⋮ Drug risk assessment with determining the number of sub-populations under finite mixture normal models ⋮ Bayesian model choice based on Monte Carlo estimates of posterior model probabilities ⋮ Hypothesis tests on mixture model components with applications in ecology and agriculture ⋮ Model weights for model choice and averaging

Uses Software

• LISREL
• EQS

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Data. A collection of problems from many fields for the student and research worker
• Statistical decision theory and Bayesian analysis. 2nd ed
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• A Monte Carlo method for high dimensional integration
• Estimating the dimension of a model
• Inference from iterative simulation using multiple sequences
• Marginal Likelihood from the Gibbs Output
• Comparing Non-Nested Linear Models
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Accurate Approximations for Posterior Moments and Marginal Densities
• On Bartlett adjustments for approximate Bayesian inference
• Practical Bayesian Density Estimation Using Mixtures of Normals
• Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
• A two‐stage estimation of structural equation models with continuous and polytomous variables
• Bayesian Density Estimation and Inference Using Mixtures
• Bayes Factors
• Latent Variable Models with Mixed Continuous and Polytomous Data
• Multivariate Normal Mixtures: A Fast Consistent Method of Moments
• Monte Carlo sampling methods using Markov chains and their applications
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
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