# Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors

@article{Qin2018TraceclassMC, title={Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors}, author={Qian Qin and James P. Hobert}, journal={J. Multivar. Anal.}, year={2018}, volume={166}, pages={335-345} }

Let $\pi$ denote the intractable posterior density that results when the likelihood from a multivariate linear regression model with errors from a scale mixture of normals is combined with the standard non-informative prior. There is a simple data augmentation algorithm (based on latent data from the mixing density) that can be used to explore $\pi$. Let $h(\cdot)$ and $d$ denote the mixing density and the dimension of the regression model, respectively. Hobert et al. (2016) [arXiv:1506.03113v2…

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