# A Quick Tour Through Prophesy¶

This tour focusses on the use of the Command Line Interface (CLI). While the tour is organised around a series of problems common in parameter synthesis, we recommend first-time users to follow the guide in a linear fashion.

## Sampling¶

A parametric model can be instantiated with different values for the parameters (instantiations). Sampling explores the parameter space (i.e. the set of all instantiations) by evaluating various instantiated models:

```
python scripts/parameter_synthesis.py load-problem model.pm property.pctl sample
```

## The solution function problem¶

This problem asks for a closed-form representation that shows how the measure-of-interest (e.g., the probability to reach a predefined set of target states) depends on the parameter values. Many probabilistic model checkers support these computations: Prophesy can be used as a uniform gateway to these model checkers:

```
python scripts/parameter_synthesis.py load-problem model.pm property.pctl compute-solution-function
```

This tells the CLI to load the problem instance described by the model file and the property file, and then to compute a solution function. The call can be adapted in several directions:

```
python scripts/parameter_synthesis.py --mc storm load-problem model.pm property.pctl compute-solution-function --export sol.out
```

This call has been adapted in two directions: We now specified the model checker to be used, in this case storm, and that the result should be exported to a file sol.out.

The computed solution function can be used to speed up sampling:

## The exact synthesis problem¶

The exact synthesis problem asks for an exact representation of all parameter instantiations that satisfy a specification. The solution function is a concise (but often very complex) solution. Prophesy currently does not support any other versions of the exact synthesis problem.

## The feasible / optimal instantiation problem¶

TBD