The Monte Carlo method is a method of estimation that uses random simulation to solve physical or mathematical problems. Named after the Monte Carlo Casino, you can think of the method as playing a game of chance many times and recording the outcomes (such as how frequently one wins or loses).

One classic example from the Wikipedia article is estimating the value of π (although there are many other ways to approximate the value of π):

For example, consider a circle inscribed in a unit square. Given that the circle and the square have a ratio of areas that is π/4, the value of π can be approximated using a Monte Carlo method:

- Draw a square on the ground, then inscribe a circle within it.
- Uniformly scatter some objects of uniform size (grains of rice or sand) over the square.
- Count the number of objects inside the circle and the total number of objects.
- The ratio of the two counts is an estimate of the ratio of the two areas, which is π/4. Multiply the result by 4 to estimate π.

You can visualize how this works by seeing the estimate get more correct as you increase the number of points:

We can generate a random point inside the unit circle by generating two random-unit values:

: random-point ( -- x y ) random-unit random-unit ;

Using the Pythagorean theorem, we can calculate the distance from the zero point. If the distance is less than or equal to 1.0, then it is inside the circle:

: inside-circle? ( x y -- ? ) [ sq ] bi@ + sqrt 1.0 <= ;

We can then estimate the value of π by computing a number of points, taking the percentage that are inside the circle and multiplying by 4:

: estimate-pi ( points -- pi-estimate ) 0 swap [ [ random-point inside-circle? [ 1 + ] when ] times ] keep /f 4 * ;

We can run this for varying numbers of points and see how it gets more accurate:

IN: scratchpad { 100 1,000 10,000 100,000 1,000,000 10,000,000 } [ estimate-pi . ] each 3.2 3.168 3.162 3.15176 3.14288 3.1418212

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