Generating random numbers

While getting truly random numbers is a difficult task, most of the Monte Carlo methods perform well with pseudo-random numbers, and this makes it easier to rerun simulations based in a seed. Practically, all modern programming languages include basic random sequences, or at least sequences good enough to produce accurate simulations.

• Python includes the random library. In the following code, we can see the basic usage of this library:

    import random as rnd

• Getting a random float between 0 and 1:

>>>rnd.random()
0.254587458742659

• Getting a random number between 1 and 100:

>>>rnd.randint(1,100)
56

• Getting a random float between 10 and 100 using a uniform distribution:

>>>rnd.uniform(10,100)
15.2542689537156

Tip

For a detailed list of methods in the random library, go to: http://docs.python.org/3.2/library/random.html

In the case of JavaScript, a more basic random function is included with the function Math.random(); for the purpose of...