MHadaptive: General Markov Chain Monte Carlo for Bayesian Inference using adaptive Metropolis-Hastings sampling. Performs general Metropolis-Hastings Markov Chain Monte Carlo sampling of a user defined function which returns the un-normalized value (likelihood times prior) of a Bayesian model. The proposal variance-covariance structure is updated adaptively for efficient mixing when the structure of the target distribution is unknown. The package also provides some functions for Bayesian inference including Bayesian Credible Intervals (BCI) and Deviance Information Criterion (DIC) calculation
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Zhang, Jincheng; Fu, Song: An efficient approach for quantifying parameter uncertainty in the SST turbulence model (2019)
- Zhang, Jincheng; Fu, Song: An efficient Bayesian uncertainty quantification approach with application to (k)-(\omega)-(\gamma) transition modeling (2018)
- Rodrigues, Eliane Regina; Achcar, Jorge Alberto: Applications of discrete-time Markov chains and Poisson processes to air pollution modeling and studies. (2013)