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  "Title": "Bayesian Logistic Regression with Heavy-Tailed Priors",
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  "Description": "Efficient Bayesian multinomial logistic regression based\non heavy-tailed (hyper-LASSO, non-convex) priors. The posterior\nof coefficients and hyper-parameters is sampled with restricted\nGibbs sampling for leveraging the high-dimensionality and\nHamiltonian Monte Carlo for handling the high-correlation among\ncoefficients. A detailed description of the method: Li and Yao\n(2018), Journal of Statistical Computation and Simulation,\n88:14, 2827-2851, <doi:10.48550/arXiv.1405.3319>.",
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