Generalised Likelihood Uncertainty Estimation (GLUE) Framework
Joost Delsman, Deltares, 2011

Generic Python framework to conduct GLUE analyses
NOTE: Package is still under construction and undocumented


Necessary:
1) model parameters, that:
- hold the statistical properties of the a priori parameter space
- hold the statistical properties of the a posteriori parameter space
- can return a random value based on the a priori parameter space
- can accept an evaluated parameter + behavioural / non-behavioural
    statement from the evaluator
    
2) a model, that:
- returns a result set based on a parameter set
- can be called successively to explore the parameter space
- resides outside GLUE
- (can even be outside python)
- function or wrapper must return a dict with parameters and
   their values, recognised by the evaluator
   
3) a model evaluator, that:
- evaluates the model result by a prescribed set of rules
- assigns behavioural / non-behavioural

4) a framework that:
- initializes a monte-carlo sequence
- for each run:
    - get parameter values
    - run the model
    - evaluate the model
    - store behavioural runs