Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), JSCS, 88:14, 2827-2851, <arXiv:1405.3319>.
|Author||Longhai Li [aut, cre] (<https://orcid.org/0000-0002-3074-8584>), Steven Liu [aut]|
|Maintainer||Longhai Li <[email protected]>|
|Package repository||View on CRAN|
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