Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation
DOI10.1016/J.CSDA.2013.11.003zbMath1506.62058OpenAlexW1996788283WikidataQ62899401 ScholiaQ62899401MaRDI QIDQ1623396
Pamela A. McCombe, Christopher C. Drovandi, Anthony N. Pettitt, Robert D. Henderson
Publication date: 23 November 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://eprints.qut.edu.au/54864/1/MUNE_Marginalisation.pdf
reversible jump Markov chain Monte Carlomodel choiceneurophysiologymarginalisationmotor neurone diseasemotor unit number estimation
Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)
Related Items (2)
Cites Work
- Unnamed Item
- The pseudo-marginal approach for efficient Monte Carlo computations
- Sequential Monte Carlo on large binary sampling spaces
- Bayesian analysis of the patterns of biological susceptibility via reversible jump MCMC sampling
- Slice sampling. (With discussions and rejoinder)
- Sequential Monte Carlo Samplers
- Approximate Bayes factors and accounting for model uncertainty in generalised linear models
- Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
- Bayesian Measures of Model Complexity and Fit
- Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes
- Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
- Motor Unit Number Estimation—A Bayesian Approach
- The elements of statistical learning. Data mining, inference, and prediction
- Deviance information criteria for missing data models
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