Tong Xin, Ke Zijun
Department of Psychology, University of Virginia, Charlottesville, VA, United States.
Department of Psychology, Sun Yat-sen University, Guangzhou, China.
Front Psychol. 2021 Mar 31;12:624588. doi: 10.3389/fpsyg.2021.624588. eCollection 2021.
Bayesian non-parametric (BNP) modeling has been developed and proven to be a powerful tool to analyze messy data with complex structures. Despite the increasing popularity of BNP modeling, it also faces challenges. One challenge is the estimation of the precision parameter in the Dirichlet process mixtures. In this study, we focus on a BNP growth curve model and investigate how non-informative prior, weakly informative prior, accurate informative prior, and inaccurate informative prior affect the model convergence, parameter estimation, and computation time. A simulation study has been conducted. We conclude that the non-informative prior for the precision parameter is less preferred because it yields a much lower convergence rate, and growth curve parameter estimates are not sensitive to informative priors.
贝叶斯非参数（BNP）建模已得到发展，并被证明是分析具有复杂结构的杂乱数据的强大工具。尽管BNP建模越来越受欢迎，但它也面临挑战。其中一个挑战是狄利克雷过程混合中精度参数的估计。在本研究中，我们专注于一个BNP增长曲线模型，并研究无信息先验、弱信息先验、准确信息先验和不准确信息先验如何影响模型收敛、参数估计和计算时间。我们进行了一项模拟研究。我们得出结论，精度参数的无信息先验不太可取，因为它产生的收敛速度要低得多，并且增长曲线参数估计对信息先验不敏感。
