Simulation for the Social Scientist

By Nigel Gilbert; Klaus G. Troitzsch | Go to book overview

Appendix C
Random number generators

Random events are usually modelled and simulated using pseudo-random number generators. If an event has to occur with probability p, then a continuous random variable which is uniformly distributed between 0 and 1 is sampled and if its realization is less than p the event occurs, otherwise the event does not occur. So the problem reduces to the problem of realizing uniformly distributed random numbers. Another related problem is the production of random variables with other distributions, such as an exponentially distributed arrival time. A continuous random variable X is exponentially distributed with parameter λ (mean 1/λ) if its probability density function is

Its distribution function Fx(x) (which yields the probability that Xx) is

This function (which is strictly increasing) has an inverse function (which is also strictly increasing):

A random quantity X with the distribution function Fx(x) can now be computed by setting

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