genericMCMC-package: Generic adaptive Monte Carlo Markov Chain sampler
In adaptMCMC: Implementation of a Generic Adaptive Monte Carlo Markov Chain Sampler
Description
Details
Author(s)
References
See Also
Description
Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.
Details
Package: adaptMCMC
Type: Package
Version: 1.4
Date: 2021-03-29
License: GPL (>= 2)
LazyLoad: yes
The workhorse function is MCMC. Chains can be updated with
MCMC.add.samples. MCMC.parallel is a
wrapper to generate independent chains on several CPU's in parallel
using parallel. coda-functions can be used after conversion
with convert.to.coda.
Author(s)
Andreas Scheidegger, andreas.scheidegger@eawag.ch or
scheidegger.a@gmail.com
References
Vihola, M. (2012) Robust adaptive Metropolis algorithm with coerced acceptance rate.
Statistics and Computing, 22(5), 997-1008. doi:10.1007/s11222-011-9269-5.
See Also
MCMC, MCMC.add.samples,
MCMC.parallel, convert.to.coda
The package HI provides an adaptive rejection Metropolis sampler
with the function arms. See also
Metro_Hastings of the MHadaptive package.
adaptMCMC documentation built on March 29, 2021, 9:11 a.m.
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Description Details Author(s) References See Also
Description
Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.
Details
| Package: | adaptMCMC |
| Type: | Package |
| Version: | 1.4 |
| Date: | 2021-03-29 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
The workhorse function is MCMC. Chains can be updated with
MCMC.add.samples. MCMC.parallel is a
wrapper to generate independent chains on several CPU's in parallel
using parallel. coda-functions can be used after conversion
with convert.to.coda.
Author(s)
Andreas Scheidegger, andreas.scheidegger@eawag.ch or scheidegger.a@gmail.com
References
Vihola, M. (2012) Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing, 22(5), 997-1008. doi:10.1007/s11222-011-9269-5.
See Also
MCMC, MCMC.add.samples,
MCMC.parallel, convert.to.coda
The package HI provides an adaptive rejection Metropolis sampler
with the function arms. See also
Metro_Hastings of the MHadaptive package.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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