Bernoulli Distribution¶
Definition¶
Let . A random variable has a Bernoulli distribution with parameter , written , if
We write . The endpoints and give degenerate distributions: is then constant, always or always .
Interpretation¶
A Bernoulli variable models a single trial with two possible results, success coded as and failure as . The one number determines the whole distribution.
The figure shows : a bar of height at the value and a bar of height at the value ; the two bars add up to . The formulas in the box are the Key Results of this page.
Coding success as makes the indicator of the event ; conversely, the indicator of any event with probability is . The label "success" is arbitrary: is again Bernoulli, with parameter . The coding pays off when trials are repeated: for independent trials , all , the sum counts the successes and has the binomial distribution.
Key Results¶
- and .
- MGF for every real .
Examples¶
Further Properties¶
An indicator of an event with probability has this distribution; sums of independent Bernoulli variables with the same success probability produce binomial counts.
Prerequisites¶
Related Concepts¶
- Indicator Random Variable
- Moment Generating Function
- Independent and Identically Distributed Random Variables
Sources¶
- Probability Theory for EOR · Week 3
- Nicky van Foreest · Probability Distributions · Refresher
Aliases: Bern(p), Bernoulli random variable, Bernoulli variable, Bernoulli r.v. Keywords: success indicator
Last updated: 20 October 2026 03:02
Bernoulli DistributionBinomial DistributionFirst Success DistributionGeometric DistributionIndividual Risk ModelDiscrete Random VariableProbability Mass FunctionIndicator Random VariableMoment Generating FunctionIndependent and Identically Distributed Random Variables