The Bayesian Lasso
@article{Park2008TheBL,
title={The Bayesian Lasso},
author={Trevor H Park and G. Casella},
journal={Journal of the American Statistical Association},
year={2008},
volume={103},
pages={681 - 686}
}The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters have independent Laplace (i.e., double-exponential) priors. Gibbs sampling from this posterior is possible using an expanded hierarchy with conjugate normal priors for the regression parameters and independent exponential priors on their variances. A connection with the inverse-Gaussian distribution provides tractable full conditional distributions. The… Expand
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