Bayesian variable selection in finite mixture of regression models with an unknown number of components
A feasible reversible jump Markov Chain Monte Carlo algorithm is implmented to Bayesian variable selection in the finite mixture models to deal with model selection and the selection of the number of mixture components simultaneously.
You can install MLModelSelection from CRAN:
install.packages("UnknownCompFMR")
You can run the simulation study in R
source("CreditGrowth_JIMF_UnknonwComp.r")
- Kuo-Jung Lee - Maintainer - Research NCKU
- Yi-Chi Chen - Statistical analysis - Research NCKU
- Martin Feldkircher - Statistical model developer - Research NCKU
- MOST, Taiwan
- etc