Results 131 to 140 of about 121,610 (166)

Personalized Nutrition Recommendations Using a Bayesian Mixture Model of Concentration Constraints and Intake Preferences. [PDF]

Stat Med
Turkia J, Schwab U, Hautamäki V.
europepmc   +1 more source

Food Security-Climate Change-National Income Nexus: Insights from GCC Countries. [PDF]

Foods
Elzaki RM.
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How plausible is my model? Assessing model plausibility of structural equation models using Bayesian posterior probabilities (BPP). [PDF]

Behav Res Methods
Pesigan IJA, Cheung SF, Wu H, Chang F, Leung SO.   +4 more
europepmc   +1 more source

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Bayesian structural equation model

WIREs Computational Statistics, 2014
Latent variables that should be measured by multiple observed variable are common in substantive research. Structural equation models (SEMs), which can be regarded as regression models with observed and latent variables, are useful models to assess interrelationships among these variables and have been widely applied to many fields.
Lee, Sik-Yum, Song, Xin-Yuan
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Bayesian Quantile Structural Equation Models

Structural Equation Modeling: A Multidisciplinary Journal, 2015
Structural equation modeling is a common multivariate technique for the assessment of the interrelationships among latent variables.
Yifan Wang, Xiang-Nan Feng, Xin-Yuan Song   +2 more
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A semiparametric Bayesian approach for structural equation models

Biometrical Journal, 2010
AbstractIn the development of structural equation models (SEMs), observed variables are usually assumed to be normally distributed. However, this assumption is likely to be violated in many practical researches. As the non‐normality of observed variables in an SEM can be obtained from either non‐normal latent variables or non‐normal residuals or both ...
Song, Xin-Yuan, Pan, Jun-Hao, Kwok, Timothy, Vandenput, Liesbeth, Ohlsson, Claes, Leung, Ping-Chung   +5 more
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Bayesian Estimation and Testing of Structural Equation Models

Psychometrika, 1999
The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples.
Scheines, R., Hoijtink, H.J.A., Boomsma, A.   +2 more
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Bayesian Semiparametric Structural Equation Models with Latent Variables

Psychometrika, 2010
Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables.
Yang, Mingan, Dunson, David B.
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Modeling Misspecification as a Parameter in Bayesian Structural Equation Models

Educational and Psychological Measurement, 2023
Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter—a parameter akin to the correlation root mean squared residual.
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