Glossary
- parameter uncertainty
An uncertainty is a parameter uncertainty if the range is continuous from the lower bound to the upper bound. A parameter uncertainty can be either real valued or discrete valued.
- categorical uncertainty
An uncertainty is categorical if there is not a range but a set of possibilities over which one wants to sample.
- lookup uncertainty
vensim specific extension to categorical uncertainty for handling lookups in various ways
- uncertainty space
the space created by the set of uncertainties
- ensemble
a python class responsible for running a series of computational experiments.
- model interface
a python class that provides an interface to an underlying model
- working directory
a directory that contains files that a model needs
- classification trees
a category of machine learning algorithms for rule induction
- prim (patient rule induction method)
a rule induction algorithm
- coverage
a metric developed for scenario discovery
- density
a metric developed for scenario discovery
- scenario discovery
a use case of EMA
- case
A case specifies the input parameters for a run of a model. It is a dict instance, with the names of the uncertainties as key, and their sampled values as value.
- experiment
An experiment is a complete specification for a run. It specifies the case, the name of the policy, and the name of the model.
- policy
a policy is by definition an object with a name attribute. So, policy[‘name’] most return the name of the policy
- result
the combination of an experiment and the associated outcomes for the experiment
- outcome
the data of interest produced by a model given an experiment