# rndBernoulli¶

## Purpose¶

Computes Bernoulli distributed random numbers.

## Format¶

r = rndBernoulli(r, c, prob)
{ r, newstate } = rndBernoulli(r, c, prob, state)
Parameters:
• r (scalar) – number of rows of the output matrix.

• c (scalar) – number of columns of the output matrix.

• prob (scalar) – probability parameter.

• state (scalar or opaque vector) –

Optional argument.

scalar case

state = starting seed value only. If -1, GAUSS computes the starting seed based on the system clock.

opaque vector case

state = the state vector returned from a previous call to one of the rnd random number functions.

Returns:
• r (RxC matrix) – Bernoulli random numbers.

• newstate (Opaque vector) – the updated state.

## Examples¶

// Bernoulli random numbers can be used to model qualitative
// binary data (i.e., yes/no, true/false), such as marital
// status.

// Set the random seed for repeatable numbers.
rndseed 723940439;

// The percentage of married people in the population we
// would like to model.
prob = 0.7;

// Create 10,000 Bernoulli random numbers
r = rndBernoulli(10000, 1, prob);

// The mean of 'r' should approximately equal 'prob'
mu = meanc(r);
print mu;

0.70270000


## Remarks¶

The properties of the pseudo-random numbers in x are:

\begin{align}\begin{aligned}E(X) = prob\\Var(X) = prob * (1 - prob)\end{aligned}\end{align}