\section{Glossary}

\textbf{Fortran}: Refers to the fortran programming language, version 90/95 specifically. \textbf{fortran} refers to the whole Fortran family, and \textbf{FORTRAN} refers to FORTRAN/77 specifically.

\textbf{Proposal}: A new value candidate obtained from the proposal distribution.

\textbf{Proposal distribution}: 
\begin{enumerate}
\item The distribution from which the next proposed value for a parameter is chosen (when using the ``Fixed proposal distribution'' proposal strategy).
\item The distribution that is used in generating proposed values for a parameter by adding a value taken from the distribution to the parameter's current value (when using the ``Random walk'' proposal strategy).
\end{enumerate}

\textbf{Frequency function} is used to refer to a frequency function or a density function that gauges the `goodness' of variable values.

\textbf{Variable} is used to refer to a data variable or an updated parameter. and the Variable class that represents these in the Generator.

\textbf{Equation}: refers to equation of functional variables. The equation is represented by an Equation object.

\textbf{Generator}: Used to refer to the modules of the software that write out the specific executable Program that carries out the simulation for a given simulation model.

\textbf{Program}: The program that is run to simulate the problem model. Synonym: generated program.

\textbf{Entity}: A data structure of the Generator representing a repetitive structure (indexing structure) of variables. For example $alpha_i$, $x_i$ both are part of the Entity indexed with $i$.

\textbf{Variable group}: All variables of a group, that is $alpha_i$ for all $i$.

\textbf{Parser}: The ComputationalModelParser class used for reading the input files for the Generator.

\textbf{Prior distribution}: The prior distribution of the parameters describes their assumed joint probability distribution before inferences based on the data are made.

\textbf{Posterior distribution}: The posterior distribution of the parameters describes their joint probability distribution after inferences based on the data are made.

\textbf{Adjacency matrix}: The adjacency matrix of a simple graph is a matrix with rows and columns labeled by graph vertices, with a 1 or 0 in position ij according to whether i and j are adjacent or not.

\textbf{Real number}: The data type for floating point numbers is called `real' in Fortran.

\textbf{Floating point number}: A computer representation of a real number with finite precision.

\textbf{Iteration}: A single round of the algorithm when all the parameters have been updated once.

\textbf{Burn-in-iterations}: The iterations that are run before any output is produced.

\textbf{Thinning factor}: The thinning factor t means that every $t^{th}$ iteration value is used in the 
output and the rest are discarded.

\textbf{Update}: Proposing a value to a parameter and then accepting it (the value changes) or discarding it (the value remains the same).

\textbf{Proposal strategy}: The proposal strategy defines how the next proposed value is generated. Possible choises are 
\begin{enumerate}
\item Fixed proposal strategy: The next proposed values for a parameter is taken from its proposal distribution.
\item Random walk: The next proposed value for a parameter is created by adding a value taken from the proposal distribution to the current value of the parameter.
\end{enumerate}

\textbf{Update strategy}: The update strategy describes how and in which way variables are updated. Only one update strategy, `sequential update', has been implemented. Random update strategy was designed but now implemented.

\textbf{Semi-Bayesian model}: A Bayesian model which allows cycles when dealing with spatial dependencies.
