Hi Ronan,

Thanks for the feedback.

> Sets will be useful in many areas, not just your project, so I think you
> should start with that.

Working on this now. Stay tuned!

> I think you should make your definition more rigorous, by giving
> symbolic names to things, to make it clearer what information each
> object requires.

I think that this is my plan. I should make this clearer in the
writeup.

> Your definition of probability spaces is a bit confusing. If you're
> using the measure-theoretic definition (and you basically have to, in
> order to handle continuous distributions correctly), you should explain
> why the measurable sets need to be specified. Anyway, an event is just
> any subset of the sample space that has a well-defined probability.
>
> For random variables, there's a problem: they're mathematically defined
> as functions from a hidden probability space into an observable sample
> space. The hidden space is required in order to handle correlations, but
> your definition doesn't seem to have an equivalent of that.

In my mind there is a probability space object that is separate from
the random variable. This contains the "hidden" part. You're right
that observables/random variables may have less information (such as
die % 2) - I often confuse things by using a random variable that is
just the identity map on the probability space.

>
> Finally, the syntax 'Practice = Rain==False or Temp > 20' cannot work:
> neither '==' nor 'or' can be altered from their builtin meaning. You
> should use something like 'practice = ~rain | (temp > 20)'. But then the
> concept of event seems redundant with that of random boolean (if rain is
> a random boolean, certainly ~rain is one too).

You're right - it's unclear if this should be an event or a random
variable. Thanks for the heads up on 'or'. I was hoping to use | for
'given' in the future. I'll figure this out when I get there. Isn't
'==' ok to use though? Isn't it __eq__? I thought that 'is' was the
forbidden one.

Keep the comments coming,
-Matt

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