论文标题
马尔可夫链蒙特卡洛方法使用概率编程（RSTAN）来优化剂量反应优化
A Markov Chain Monte-Carlo Approach to Dose-Response Optimization Using Probabilistic Programming (RStan)
论文作者
论文摘要
提出并在R中实施了分层逻辑回归贝叶斯模型，以模拟与任何给定剂量的某种药物相对应的患者改善的可能性。 RSTAN用于通过马尔可夫链蒙特卡洛（MCMC）采样从后验分布中获取样品。检查了选择不同的先前分布族的效果，最后，在RSTAN和其他两个环境中比较了后验分布，即PYMC和Agenarisk。
A hierarchical logistic regression Bayesian model is proposed and implemented in R to model the probability of patient improvement corresponding to any given dosage of a certain drug. RStan is used to obtain samples from the posterior distributions via Markov Chain Monte-Carlo (MCMC) sampling. The effects of selecting different families of prior distributions are examined and finally, the posterior distributions are compared across RStan, and two other environments, namely PyMC, and AgenaRisk.
