| FBMS-package {FBMS} | R Documentation |
Flexible Bayesian Model Selection and Model Averaging
Description
Implements MJMCMC (mode jumping MCMC) described in Hubin and Storvik (2018) <doi:10.1016/j.csda.2018.05.020> and GMJMCMC (genetically modified MJMCMC) described in Hubin et al. (2021) <doi:10.1613/jair.1.13047> algorithms as well as the subsampling counterpart described in Lachmann et al. (2022) <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.
Author(s)
Maintainer: Jon Lachmann jon@lachmann.nu
Authors:
Jon Lachmann jon@lachmann.nu
Aliaksandr Hubin aliaksah@math.uio.no
Other contributors:
Florian Frommlet florian.frommlet@meduniwien.ac.at [contributor]
Geir Storvik geirs@math.uio.no [contributor]
References
Lachmann, J., Storvik, G., Frommlet, F., & Hubin, A. (2022). A subsampling approach for Bayesian model selection. International Journal of Approximate Reasoning, 151, 33-63. Elsevier.
Hubin, A., Storvik, G., & Frommlet, F. (2021). Flexible Bayesian Nonlinear Model Configuration. Journal of Artificial Intelligence Research, 72, 901-942.
Hubin, A., Frommlet, F., & Storvik, G. (2021). Reversible Genetically Modified MJMCMC. Under review in EYSM 2021.
Hubin, A., & Storvik, G. (2018). Mode jumping MCMC for Bayesian variable selection in GLMM. Computational Statistics & Data Analysis, 127, 281-297. Elsevier.