# use parametric in a sentence.

Ad hoc polymorphism ∈ OOP*Parametric* polymorphism ∈ FP

Is this the same as non-*parametric* learning?

Only few methods such as K nearest neighbors are non *parametric*, rest all methods such as SVM's, decision trees are all *parametric*.

Ah I didn't know that there were some special /slashtags, this is even a *parametric* slashtag...

For the same reason, it's "machine learning" rather than "non *parametric* statistics".

The non-*parametric* stats functions in R are better than Py

Might you be interested in a dash of *parametric* polymorphism for type safety?

Go doesn't have *parametric* polymorphism.

Bah, if they were explicit, they wouldn't be nearly as useful for *parametric* polymorphism.

Since Haskell is statically typed, I think it's a feature, not a bug, that type *parametricity* is implemented at compile time.

Also, I think what you're describing is the difference between *parametric* polymorphism and ad-hoc polymorphism.

I am not even sure what implementing it for a *parametric* type means...

Languages like SML and OCaml are pretty clean, pretty simple and have an awesome *parametric* polymorphism system.

No, non-*parametric* learning is an unrelated topic, which seeks to estimate probability distributions without using "*parametric*" (= having known structure) models.

BTW, Haskell only has (bounded) *parametric* polymorphism.

EDIT: Take a look at this article, which explores developing a library that adds runtime-checked type *parametric* functions.

Can you recommend a book on advanced statistical techniques or non-*parametric* statistics that's written mostly in English with a minimum of jargon?