The exponential distribution is a "waiting time" distribution. Suppose events occur independently and randomly through time with an average time between events of β. The time spent waiting for an event to occur is an exponential(β) random variable.
The exponential distribution is memoryless. This means that the probability of having to wait a further amount of time is independent of the time already spent waiting.
Parameter | Range | Description |
---|---|---|
β | β > 0 | Scale parameter |
Probability Density Function
Support
Mean
Variance
Example | β |
---|---|
A radioactive substance emits two alpha particles every second on average. Let X be the waiting time for the next particle to be emitted. | 0.5000 |
On a particular night, a shooting star occurs every five minutes on average. Let X be the waiting time for the next shooting star. | 5.000 |
A hitchhiker sees one car every 10 minutes on average. Let X be the waiting time for the next car. | 10.00 |
X ∼ Exponential(β)
E(X) = , Var(X) =
Note that the standard deviation equals the expected value. All exponential distributions have the same shape, and differ only in scale.
Note that the standard deviation equals the expected value. All exponential distributions have the same shape, and differ only in scale.
The graph above displays the survival function S(x) = P(X > x) = 1 - F(X), where F(x) is the cumulative distribution function (cdf).
Survival functions are used in survival analysis, a branch of statistics concerned with the expected duration until an event occurs such as death or the failure of a mechanical system.
The graph above displays the hazard function h(x). This equals f(x)/S(x), where f(x) is the pdf and S(x) = P(X > x) is the survival function.
Note that:
- The hazard function is constant and independent of time, since the probability of an event occurring is independent of the time already spent waiting.
- The hazard function is inversely proportional to the mean time β between events.
The illustration above shows events occuring randomly in time independently of other events, with a mean time between events of β. The time at which the first event occurs has an exponential(β) distribution.
The simulation above shows an event occuring randomly in time independently of other events, with a mean time between events of β. The light blue line shows the time at which the first event occurs, which has an exponential(β) distribution. The histogram accumulates the results of each simulation.