Applied Bayesian Hierarchical Methods

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A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016, unknown): “Hierarchical modeling can be viewed from a more frequentist [16, 17] or a more Bayesian perspective [18, 19].”
Cognitive model decomposition of the BART: Assessment and application (2011, background): “Third, posterior distributions automatically and naturally give an idea about the uncertainty in the inference (Congdon, 2010).”
A general and simple method for obtaining R2 from generalized linear mixed‐effects models (2013, methodology): “Indeed, LMMs and GLMMs are becoming a part of standard methodological tool kits in biological sciences (Bolker et al. 2009), as well as in social and medical sciences (Gelman & Hill 2007; Congdon 2010; Snijders & Bosker 2011).”
A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes (2015, methodology): “Bayesian estimation of SEMs (Kaplan & Depaoli, 2012; Lee, 2007; Muthén & Asparouhov, 2012a; Scheines, Hoijtink, & Boomsma, 1999) and MLMs (Congdon, 2010; Gelman & Hill, 2007; Lynch, 2007) is a prevalent methodological topic because Bayesian methods are useful for solving problems that are unique to ”
Neglected biological patterns in the residuals (2011, background): “In certain situations heteroscedasticity may also be referred to as variance heterogeneity (Congdon 2010), but for the sake of simplicity we will only use the term heteroscedasticity here.”