Abstract: A linear model is $Y=X\beta+\varepsilon$ where $Y$, the response vector, is Gau\
ssian $(X\beta,\sigma I)$.
A mixed model incorporates two random variables: $B$ the random effects and $Y\
$ the response vector.
In a linear mixed model the unconditional distribution of $B$ and the condition\
al distribution $(Y| B = b)$ are both Gaussian distributions,
$(Y| B =b)\sim N(X\beta+Zb,\sigma I)$ and $B \sim N (0,\Sigma\theta)$.
Objective: Parameter estimation by maximum likelihood.
Problem: Understand the algorithm.