= Gaussian linear mixed model
{c}
{title2=$Y=X\beta+Zb+\varepsilon$}
A Gaussian linear mixed model combines <fixed effects>, <random effects> with a <normal distribution>, and independent <Gaussian noise>. For $b\sim N(0,D)$ and $\varepsilon\sim N(0,R)$ independently, its marginal distribution is $Y\sim N(X\beta,V)$ with $V=ZDZ^T+R$. The <random effects> induce dependence among observations sharing their design columns. A <random-intercept linear mixed model> and a <random-slope linear mixed model> are special cases.
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