One extremely simple way to model probability is with a list of the universe of all events that can happen, along with the chances of each.

We’ll use `Rational`

(from `Data.Ratio`

) here because it helps us avoid floating point arithmetic issues and so on. Realistically, we’d want to restrict `a`

with some kind of `Event`

typeclass, and we’d want to bound our probabilities to the range \[ [0, 1] \] but this model is good enough to show how to build a monad out of a bunch of events-with-likelihoods.

It’s going to be useful to have a nicely-formatted way of printing out the chance of each event happening, so let’s do that too:

```
display :: Show a => Prob a -> IO ()
display (Prob dist) = do
mapM_ putStrLn [show x ++ ": " ++ showNum p | (x, p) <- dist]
where showNum p = show (Ratio.numerator p) ++ "/" ++ show (Ratio.denominator p)
```

At this point we can start building up a bunch of instances. `Functor`

is reasonably straightforward: we just map the function over each event, almost like the `fmap`

instance for lists.

One thing to note here is that our `do`

block is working with lists, so we could also have used list comprehensions like below. However, I’ll favor `do`

since I think it’s a smidge cleaner.

Moving on to applicatives. `pure`

is also somewhat common sense: we take the single event given, and give it all the probability mass (its chances of occurring are 100%).

A reasonable instance definition of `<*>`

would take all the event-altering functions in `a`

and apply them to all the events in `b`

. We multiply probabilities because this reduces them in just the right way to maintain the correct overall sum total probability mass. If we take two coins, the chance of flipping heads on either is \[ 1/2 \] but the chance of getting heads on both is \[ 1/4 \]. You can also think of applicatives as doing something like the product of lists here, so it makes sense that we’d also want the product of probabilities.

Now for the monad instance. `return`

is just `pure`

.

Bind is a bit more interesting. As with `<*>`

, we’re taking the list of all events out of our prior `Prob`

and as with `fmap`

we apply `f`

to each piece. We then take our probabilities and multiply, along with passing back the newly-created event (I called it `y`

).

What can we do with this kind of simple monad for managing probabilities? Let’s say we want to model a fair coin in a simple way. We can use this ADT:

The instances for `Bounded`

and `Enum`

now let us assign an equal chance to each constructor of the type.

```
enumerateProb :: (Enum a, Bounded a) => Prob a
enumerateProb = equalChance [minBound..maxBound]
equalChance :: [a] -> Prob a
equalChance xs = Prob [(x, chance) | x <- xs]
where chance = 1 % length xs
a % b = fromIntegral a Ratio.% fromIntegral b
```

This makes it trivial to create more ADTs where every constructor has the same likelihood of showing up:

Another kind of thing we can do with `Prob`

is basic calculations you’d expect to be able to do with any sort of probabilistic model. For example, we can get the expected value of some set of events that we can give `Fractional`

value to.

```
expected :: (Fractional a) => Prob a -> a
expected = sum . map (\(x, p) -> x * fromRational p) . runProb
```

If I now say that I’ll give you $2 for a coin flip that’s heads if you give me $1 for every flip that’s tails, you can figure out that your maximum reasonable buy-in price per round should be 50 cents.