N.A. . Aziz, Z. Ali, N.M. Nor, A. Baharum and M. Omar, Modeling multinomial logistic regression on characteristics of smokers after the smoke-free campaign in the area of Melaka, AIP Conf. Proc. 1750(1) (2016) 60020.
 G.C. Cawley, N.L.C. Talbot and M. Girolami, Sparse multinomial logistic regression via bayesian l1 regularisation,
Adv. Neural Inf. Process. Syst. 19 (2007) 209.
 A.M. El-Habil, An application on multinomial logistic regression model, Pakistan J. Statist. Oper. Res. 8(2) (2012)
 S. Fr¨uhwirth-Schnatter and R. Fr¨uhwirth, Data augmentation and MCMC for binary and multinomial logit
models, Statist. Modell. Regression Structures (2010) 111–132.
 J. Geweke, Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments, Federal
Reserve Bank of Minneapolis, Research Department Minneapolis, 148 (1991).
 P.J. Green, K. Latuszy´nski, M. Pereyra and C.P. Robert, Bayesian computation: a summary of the current state,
and samples backwards and forwards, Stat. Comput., 25(4) (2015) 835–862. S. Kwon, D. Kim and S. Lee, An efficient algorithm for the non-convex penalized multinomial logistic regression,
Commun. Statist. Appl. Methods 27(1) (2020) 129-–140.
 R.M. Neal, Slice sampling, Ann. Statist. 13(3) (2003) 705–741.
 C. Robert and G. Casella, A short history of Markov chain Monte Carlo: Subjective recollections from incomplete
data, Statist. Sci. 26(1) (2011) 102–115.