The multinomial probit Bayesian additive regression trees (MPBART) framework was proposed by Kindo et al. (KD), approximating the latent utilities in the multinomial probit (MNP) model with BART (Chipman et al. 2010). Compared to multinomial logistic models, MNP does not assume independent alternatives and the correlation structure among alternatives can be specified through multivariate Gaussian distributed latent utilities. We introduce two new algorithms for fitting the MPBART and show that the theoretical mixing rates of our proposals are equal or superior to the existing algorithm in KD. Through simulations, we explore the robustness of the methods to the choice of reference level, imbalance in outcome frequencies, and the specifications of prior hyperparameters for the utility error term. The work is motivated by the application of generating posterior predictive distributions for mortality and engagement in care among HIV-positive patients based on electronic health records (EHRs) from the Academic Model Providing Access to Healthcare (AMPATH) in Kenya. In both the application and simulations, we observe better performance using our proposals as compared to KD in terms of MCMC convergence rate and posterior predictive accuracy.

翻译：由Kindo等人(KD)提出,与BART(Chipman等人,2010年)相比,Kindo等人(KD)提出了多名生原生原生的重金回归树(MPBART)框架,与BART(Chipman等人,2010年)相比,多名生原生原生原生原生原生(MNP)模型中的潜在公用事业(MNP)模式(Chipman等人,2010年)。与多名化后勤模型相比,MNP并不假设独立的替代品,替代品之间的相关结构可以通过多变高斯分布式潜在公用事业来具体确定。我们引入了两种新的算法来安装MPBART,并表明我们提案的理论混合率与KD的现有算法相同或更高。我们通过模拟,探索选择参考水平、结果频率失衡和以往超参数术语术语的规格方法的稳健性性。这项工作的动力是应用“肯尼亚提供保健的学术模型”的理论模型(EHRs)来生成艾滋病毒阳性病人的死亡率和护理的预测分布。在应用与KMMD术语中的精确度预测率。我们观测在应用了“海市”预测率。