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.
- Corey Chivers
- Date of publication
- 2012-03-24 17:49:17
- Corey Chivers <firstname.lastname@example.org>
- GPL (>= 3)
- Bayesian Credible Interval
- A sample object created by running Metro_Hastings().
- Thin an MCMC object to reduce autocorrelation.
- Markov Chain Monte Carlo for Bayesian Inference using...
- General Markov Chain Monte Carlo for Bayesian Inference using...
- Plot MCMC results of a call to Metro_Hastings().
- Positive Definite Matrixes
Files in this package