eve11: (Default)
Anything up with that? My co-workers are computer scientists and apparently category theory is useful for such study as related to computer languages like Haskell.

There is a parallel out there between categories and probability distributions.... I wonder if there is an application of 'functors' and isomorphism that would describe sufficiency in terms that computer scientists understand.

This is a note to myself to check this out later. I see for example that categories might need to include some notion of sub-probabilities in order to be "complete" in some sense, and that this means that a category theory relationship to probability is not necessarily one-to-one or whatever. I'm curious though, if there may be a category theoretic basis of applied statistics, that would have different implications of inference than using straight probability theory?

And could this possibly extend to continuous measurement?

Why am I thinking so hard on a Sunday? I have fanfic to write, damn it.

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eve11: (Default)
eve11

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