This normalisation requirement can cause difficulties, and unlike statistical discriminant analysis, variables cannot be selected in a computationally efficient way with mathematical programming ...

It is shown that the integer linear programming problem with a fixed number of variables is polynomially solvable. The proof depends on methods from geometry of numbers.

Grain includes functional programming features (e.g., type inference, pattern matching, closures) while allowing mutable variables.

By integrating probability theory into programming constructs, these languages enable researchers to succinctly encode uncertainty and reason about latent variables using robust computational methods.