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A Java Programmer’s Guide to Random Numbers. Part 2: Not just coins and dice

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Binomial Distribution

The probability of a coin flip coming up heads is 0.5. This is pretty simple - we can just use the uniform distribution to simulate a single coin flip. But what is the probability of getting exactly 7 heads from 10 coin flips? This is where the Binomial Distribution comes in. It provides probabilities for the outcome of a series of trials, where each trial has two possible results - success or failure. The binomial distribution tells us that the probability of obtaining heads 7 times from 10 fair coin flips is about 0.12.

Suppose, for example, we wanted to randomly generate the number of times the number 6 occurred in 100 dice rolls. We could simulate the full series of trials using a uniform RNG, but this is cumbersome:

Random rng = new MersenneTwisterRNG();
int sixes = 0;
for (int i = 0; i < 100; i++)
    int diceRoll = rng.nextInt(6) + 1;
    if (diceRoll == 6))
System.out.println("No. of 6s from 100 rolls is " + sixes);

It is much easier, and more efficient, to use the binomial distribution. If we consider an occurrence of a 6 to be a “success”, then the probability of succes is 1/6 (or ~0.1667). If we plug this value and the number of trials (100) into the Uncommons Maths BinomialGenerator class, we can get our answer from a single method call:

Random rng = new MersenneTwisterRNG();
BinomialGenerator gen = new BinomialGenerator(100, 1d/6, rng);
int sixes = gen.nextValue();
System.out.println("No. of 6s from 100 rolls is " + sixes);
Poisson Distribution

Suppose a telephone call centre, between 2pm and 4pm in the afternoon, receives calls at average of 3 per minute. What is the probability that we receive exactly 5 calls in the next minute? This is the kind of question that we can answer with the Poisson Distribution. The Poisson distribution is not dissimilar to the Binomial distribution, as you will see if you plot them. The difference is that the Poisson distribution is concerned with how many independent events occur within a set interval. Whereas values from a binomial distribution have an upper bound determined by the number of trials, there is no upper bound for a Poisson distribution. The probability of 5 calls in a minute when the average is 3 is around 0.17 according to the Poisson distribution.

In simulation applications we can use the PoissonGenerator class (its single parameter is the mean) to determine how many times something happens within a given interval.

Exponential Distribution

The Exponential Distibution is related to the Poisson Distribution. Rather than modelling how many events occur in a fixed period of time, it models the period of time between two independent events that occur at a given average rate.

Suppose you wanted to simulate some random event that happened on average 10 times a minute - perhaps you are simulating load for a server. You could simply generate events at the constant rate of exactly one every 6 seconds. But the real world is rarely this metronomic. A more realistic approach would be to generate events randomly, using an exponential distribution (provided by the ExponentialGenerator class) to determine the intervals between events:

final long oneMinute = 60000;
Random rng = new MersenneTwisterRNG();

// Generate events at an average rate of 10 per minute.
ExponentialGenerator gen = new ExponentialGenerator(10, rng);
boolean running = true;
while (true)
    long interval = Math.round(gen.nextValue() * oneMinute);

    // Fire event here.


Published at DZone with permission of its author, Dan Dyer.

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