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Significant speed improvements for Bayesian computations using a new parallelized computing approach (Asparouhov & Muthén, 2019a). In addition, Bayesian algorithms have been reorganized to produce faster computations.
Expanded Bayesian fit statistics in 3 areas: improved posterior predictive p-values (PPP) when there are missing data, Bayesian CFI/TLI/RMSEA including confidence intervals, and a Bayesian version of the Wald test of parameter restrictions using the MODEL TEST command.
Mplus模型包括连续的潜变量、分类潜变量、连续变量和类别潜变量的组合。上图中,圆柱A描述只有潜在连续变量的模型。圆柱B描述只有特定潜变量的模型。完整的建模框架描述了连续变量和类别变量相结合的模型。上图表明,Mplus估计的描述个体水平的多层次模型(内部)和集群水平(之间)的变量。
XWITH for Bayes. This avoids slow computations using ML when many XWITH terms are included. ML bootstrapping to get non-symmetric confidence intervals is not needed with Bayes because such intervals are part of its estimation.
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