Open-source code for MATLAB programing describing a variable
selection procedure under a Bayesian framework to explore the plausibility of
alternative linear regression models that include different explanatory variables, and
assess the associated uncertainty.
Bayesian variable selection treats the regression model itself as random
among all possible models with different sets of variables. The implementation of Bayesian
variable selection is via the reversible jump Markov chain Monte Carlo (rjMCMC) procedure.<div><div><br></div><div>Open-source code for R programing describing the use of boosted regression tree and random forest approaches are also provided to check if there are any nonlinear effects missed by Bayesian variable selections. </div></div
