Sun Nov 10 21:21:41 UTC 2019

```Benjamin,

I believe that with the right monad, you won't need to think about side-effects, at least not the side-effects that are manual fiddling with registering variables. Haskell should do that for you. It seems to me what you really need is a strictness analyzer together with the appropriate re-write rules that push the call to monadic actions as deep into the probabilistic model as possible. But initially you said you want to avoid source-to-source translations.

Indeed your mention of splitting the random number generator seems to buy laziness, judging by the documentation of MonadInterleave in the MonadRandom package. But looking at the definition of interleave for RandT you can see that the monadic computation is still executed, only the random number generator state is restored afterwards. In particular, side effects of the inner monad are always executed. (Or was I using it wrong?) Hence from what I've seen my judgement is that state transformer monads are a dead end.

The idea with changing a -> m b to m a -> m (m b) seemed promising as well. You can easily make a Category instance for the type

C a b = m a -> m (m b)

so that composition of arrows in C does not necessarily execute the monadic computation. However, functions of the Arrow instance (e.g. 'first') must look at the input monad action in order to turn C a b into C (a,c) (b,c), at least I could not think of another way.

Sorry for writing so destructive posts. In mathematics it is generally easier to find counterexamples than to write proofs.

Olaf

> Am 07.11.2019 um 17:56 schrieb Benjamin Redelings <benjamin.redelings at gmail.com>:
>
> Hi Olaf,
>
>
> 1. Mainly, I am _assuming_ that you can implement a lazy probability monad while ignoring random number generators.  So,       monad should be commutative, and should have the second property of laziness that you mention.
>
> (As an aside, I think the normal way to do this is to implement a function that splits the random number generator whenever you perform a lazy random computation using function: split :: g -> (g,g).  My hack is to present that we have a hardware instruction that generates true random numbers, and then put that in the IO Monad, and then use unsafeInterLeaveIO.  However, I would have to think more about this.)
> 2. My question is really about how you can represent side effects in a lazy context.  Thus the monad would be something like
> EffectMonad = (a, Set Effect),
>
> where Effect is some ADT that represents effects.  Each effect represents some action that can be undone, such as registering a newly created random variable in the list of all random variables.
> This seems to be easy in a strict context, because you can change a function a->b that has effects into a-> EffectMonad b.  Then your interpreter just needs to modify the global state to add the effects from the
> interpreter state1 (x <<= y) = let (result1,effects1) = interpreter state1 x
>                                   state2 = state1 `union` effects1
>                                in interpreter state2 (y result1)
>
> However, with a lazy language I think this does not work, because we do not want to include "effects1" unless the "result1" is actually consumed.
>
> In that context, I think that a function (a->b) would end up becoming
>
>
> The argument 'a' changes to EffectMonad 'a' because the function itself (and not the interpreter) must decide whether to include the effects of the input into the effects of the output.  The output changes to EffectMonad (EffectMonad b) so that the result is still of type (EffectMonad b) after the result is unwrapped.
>
> Does that make more sense?
> -BenRI
>
>
>
> On 10/17/19 4:03 PM, Olaf Klinke wrote:
>> Hi Benjamin,
>>
>> Your example code seems to deal with two distinct types:
>> The do-notation is about the effects monad (on the random number generator?) and the `sample` function pulls whatever representation you have for an actual probability distribution into this effect monad. In my mental model, the argument to `sample` represents a function Double -> x that interprets a number coming out of the standard random number generator as an element of type x.
>> I suggest to consider the following two properties of the mathematical probability monad (a.k.a. the Giry monad), which I will use as syntactic re-write rules in your modeling language.
>>
>> The first property is Fubini's Theorem. In Haskell terms it says that for all f, a :: m x and b :: m y the two terms
>>
>> do {x <- a; y <- b; f x y}
>> do {y <- b; x <- a; f x y}
>>
>> are semantically equivalent. (For state monads, this fails.) Monads where this holds are said to be commutative. If you have two urns, then drawing from the left and then drawing from the right is the same as first drawing from the right and then drawing from the left. Using Fubini, we can swap the first two lines in your example:
>>
>> model = do
>>    cond <- bernoulli 0.5
>>    x <- normal 0 1
>>    return (if cond == 1 then x else 0)
>>
>> This desugars to
>>
>> bernoulli 0.5 >>= (\cond -> normal 0 1 >>= (\x -> return (if cond == 1 then x else return 0)))
>> bernoulli 0.5 >>= (\cond -> fmap (\x -> if cond == 1 then x else 0) (normal 0 1))
>>
>> The second property is a kind of lazyness, namely
>>
>>     fmap (const x) and return are semantically equivalent.
>>
>> which holds for mathematical distributions (but not for state monads). Now one could argue that in case cond == 0 the innermost function is constant in x, in which case the whole thing does not depend on the argument (normal 0 1). The Lemma we need here is semantic equivalence of the following two lambda terms.
>>
>> \cond -> \x -> if cond == 1 then x else 0
>> \cond -> if cond == 1 then id else const 0
>>
>> If the above is admissible then the following syntactic transformation is allowed:
>>
>> model = do
>>    cond <- bernoulli 0.5
>>    if cond == 1 then normal 0 1 else return 0
>>
>> which makes it obvious that the normal distribution is only sampled when needed. But I don't know whether you would regard this as the same model. Notice that I disregarded your `sample` function. That is, my monadic language is the monad of probabilites, not the monad of state transformations of a random number generator. Maybe you can delay using the random number generator until the very end? I don't know the complete set of operations your modeling language sports. If that delay is possible, then maybe you can use a monad that has the above two properties (e.g. a reader monad) and only feed the random numbers to the final model. As proof of concept, consider the following.
>>
>> type Model = Reader Double
>> model :: Model Int
>> model = do
>>   x <- reader (\r -> last [1..round (recip r)])
>>   cond <- reader (\r -> r > 0.5)
>>   return (if cond then x else 0)
>>
>> runReader model is fast for very small inputs, which would not be the case when the first line was always evaluated.
>>
>> Cheers,
>> Olaf
>>

```