The purpose of this repository is to showcase a way of expressing the Monty Hall problem in a way that closely resembles a natural language description of the problem. That is, we would like to be able to write something like the query expression below, which would compute the probability of winning when switching doors:

from doorWithPrize in OneOf(doors)
from doorPickedByContestant in OneOf(doors)
let doorsQuizMasterCouldOpen = doors.Without(doorWithPrize, doorPickedByContestant)
from doorOpenedByQuizMaster in OneOf(doorsQuizMasterCouldOpen)
let doorContestantCouldSwitchTo = doors.Without(doorPickedByContestant, doorOpenedByQuizMaster).First()
select doorContestantCouldSwitchTo == doorWithPrize

If one is not familiar with LINQ queries or only has seen it used with IEnumerable<T>, then the query might be hard to read or understand. The example is also missing things like the definitions of OneOf(...) and .Without(...).

The first step to understanding the example query is knowing what each part in the from clause represents. In this example, we interpret each from clause to define an outcome of a random variable. That is, the clause

... from x in X ...

could be read as:

Let x be an outcome of the random variable X.

The second step is understanding the let clause. The let clause allows us to store the result of a sub-expression in a variable. In the example query, let is used to give a name to sub-expressions in order to make the query easier to read. For example, instead of

...
let doorsQuizMasterCouldOpen = doors.Without(doorWithPrize, doorPickedByContestant)
from doorOpenedByQuizMaster in OneOf(doorsQuizMasterCouldOpen)
...

we could have also written:

...
from doorOpenedByQuizMaster in OneOf(doors.Without(doorWithPrize, doorPickedByContestant))
...

The third and final step is understanding what the select clause does. The select clause allows us to combine and transform outcomes from one or more random variables into a new outcome. In other words, with the select clause we are effectively defining a new random variable. The example query above defines a random variable with two possible outcomes: true if the contestant wins by switching doors and false if the contestant loses.

The key ingredients needed for enabling expressive queries such as the example listed above are:

• a suitable data structure to represent random variables, and
• suitable definitions for Select and SelectMany methods that operate on the data structure.

The solution here uses vectors to represent random variables, where scalar components of these vectors represent the probability of occurrence of possible outcomes. For example, a fair coin could be represented as follows:

var fairCoin = 0.5.Of("head") + 0.5.Of("tails");
Console.WriteLine(fairCoin); // prints: {head: 0.5, tails: 0.5}

The idea of using vectors as to represent random variables was taken from the talk Commutative Monads, Diagrams and Knots presented by Dan Piponi at the International Conference on Functional Programming (ICFP), Edinburgh 2009. Please see this talk for more information, including suitable definitions for Select (called fmap in the talk) and SelectMany (which can be defined in terms of Select and a function called join).

The repository consists of the following projects:

• MontyHallLINQ: The main program.
• VectorSpace: Library that provides the Vector<T> class for representing probabilities as vectors.
• VectorSpace.Tests: Test suite for VectorSpace.