BMS toolbox for Matlab: Bayesian Model Averaging (BMA)
Bayesian Model Averaging for linear models under Zellner's g prior. Options include: fixed (BRIC, UIP, ...) and flexible g priors (Empirical Bayes, hyper-g), 5 kinds of model prior concepts, and model sampling via model enumeration or MCMC samplers (Metropolis-Hastings plain or reversible jump). Post-processing allows for inference according to different concepts (likelihood vs MCMC-based) and for plotting (posterior model size and coefficient densities, best models, model convergence, BMA comparison).
Needs the R D-COM interface or RAndFriends installed.
Works for Matlab 6.5 and later
For more details see:
http://bms.zeugner.eu/matlab/
Cite As
stz Zeugner (2024). BMS toolbox for Matlab: Bayesian Model Averaging (BMA) (https://www.mathworks.com/matlabcentral/fileexchange/29326-bms-toolbox-for-matlab-bayesian-model-averaging-bma), MATLAB Central File Exchange. Retrieved .
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