# Geometric ergodicity of the Bayesian lasso

@article{Khare2013GeometricEO, title={Geometric ergodicity of the Bayesian lasso}, author={Kshitij Khare and James P. Hobert}, journal={Electronic Journal of Statistics}, year={2013}, volume={7}, pages={2150-2163} }

Consider the standard linear model y = X +✏ , where the components of ✏ are iid standard normal errors. Park and Casella [14] consider a Bayesian treatment of this model with a Laplace/Inverse-Gamma prior on (,). They introduce a Data Augmentation approach that can be used to explore the resulting intractable posterior density, and call it the Bayesian lasso algorithm. In this paper, the Markov chain underlying the Bayesian lasso algorithm is shown to be geometrically ergodic, for arbitrary…

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