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\subsection{Global vs local variable partitioning}  The data that are fitted with \textbf{shape} are typically split into sets, with each set corresponding to a different observing run (for the same object). Correspondingly, the models that are fit to these data have both global and local variables, meaning there are parameters that are tied to the set, and parameters that are tied to the object itself. For example, how far off the object's center of mass is from the predicted center of mass is date-specific. Intelligent grouping of local and global parameters can potentially lead to a drastic computation time decrease.   \newcommand\coolover[2]{\mathrlap{\smash{\overbrace{\phantom{%  \begin{matrix} #2 \end{matrix}}}^{\mbox{$#1$}}}}#2}   \newcommand\coolunder[2]{\mathrlap{\smash{\underbrace{\phantom{%  \begin{matrix} #2 \end{matrix}}}_{\mbox{$#1$}}}}#2}  \newcommand\coolleftbrace[2]{%  #1\left\{\vphantom{\begin{matrix} #2 \end{matrix}}\right.}  \newcommand\coolrightbrace[2]{%  \left.\vphantom{\begin{matrix} #1 \end{matrix}}\right\}#2}  \begin{document}  \[ \vphantom{% phantom stuff for correct box dimensions  \begin{matrix}  \overbrace{XYZ}^{\mbox{$R$}}\\ \\ \\ \\ \\ \\   \underbrace{pqr}_{\mbox{$S$}}  \end{matrix}}%  \begin{matrix}% matrix for left braces  \vphantom{a}\\   \coolleftbrace{A}{e \\ y\\ y}\\  \coolleftbrace{B}{y \\i \\ m}  \end{matrix}%  \begin{bmatrix}  a & \coolover{R}{b & c & d} & x & \coolover{Z}{x & x}\\  e & f & g & h & x & x & x \\  y & y & y & y & y & y & y \\  y & y & y & y & y & y & y \\  y & y & y & y & y & y & y \\  i & j & k & l & x & x & x \\  m & \coolunder{S}{n & o} & \coolunder{W}{p & x & x} & x  \end{bmatrix}%  \begin{matrix}% matrix for right braces   \coolrightbrace{x \\ x \\ y\\ y}{T}\\  \coolrightbrace{y \\ y \\ x }{U}  \end{matrix}\]  \end{document}