Efficient model comparison techniques for models requiring large scale data augmentation

DOI10.1214/17-BA1057zbMath1407.62085MaRDI QIDQ1631557

Trevelyan J. McKinley, Simon E. F. Spencer, Naif Alzahrani, Peter Neal, Panayiota Touloupou

Publication date: 6 December 2018

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

Full work available at URL: https://projecteuclid.org/euclid.ba/1493431262

zbMATH Keywords

model selectionimportance samplingmarginal likelihoodepidemicsmodel evidence

Mathematics Subject Classification ID

Epidemiology (92D30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (5)

Efficient Bayesian model choice for partially observed processes: with application to an experimental transmission study of an infectious disease ⋮ Identification of the relative timing of infectiousness and symptom onset for outbreak control ⋮ Bayesian model discrimination for partially-observed epidemic models ⋮ A model selection approach for variable selection with censored data ⋮ Bayes factors for partially observed stochastic epidemic models

Uses Software

• tsbridge

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Nonparametric regression using Bayesian variable selection
• Epidemics with two levels of mixing
• Marginal Likelihood from the Gibbs Output
• Bayesian inference for generalized linear mixed models
• A Generalized Stochastic Model for the Analysis of Infectious Disease Final Size Data
• Marginal Likelihood Estimation via Power Posteriors
• Accurate Approximations for Posterior Moments and Marginal Densities
• A regression model for time series of counts
• On Gibbs sampling for state space models
• Analyses of Infectious Disease Data from Household Outbreaks by Markov Chain Monte Carlo Methods
• Transmission of Pneumococcal Carriage in Families: A Latent Markov Process Model for Binary Longitudinal Data
• Marginal Likelihood From the Metropolis–Hastings Output
• Weighted Average Importance Sampling and Defensive Mixture Distributions
• Bayes Factors
• Equation of State Calculations by Fast Computing Machines
• Computing marginal likelihoods from a single MCMC output
• MCMC for Integer-Valued ARMA processes
• Exact Bayesian Inference and Model Selection for Stochastic Models of Epidemics Among a Community of Households
• Monte Carlo sampling methods using Markov chains and their applications
• Exact Bayesian inference via data augmentation

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