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Exponential Distribution[edit]

Probability density function
Probability density function
Cumulative distribution function
Cumulative distribution function
Parameters λ > 0 rate, or inverse scale
Support x ∈ [0, ∞)
PDF λ e−λx
CDF 1 − e−λx
Mean λ−1
Median λ−1 ln 2
Mode 0
Variance λ−2
Skewness 2
Ex. kurtosis 6
Entropy 1 − ln(λ)

Exponential distribution refers to a statistical distribution used to model the time between independent events that happen at a constant average rate λ. Some examples of this distribution are:

  • The distance between one car passing by after the previous one.
  • The rate at which radioactive particles decay.

For the stochastic variable X, probability distribution function of it is:

and the cumulative distribution function is:

Exponential distribution is denoted as , where m is the average number of events within a given time period. So if m=3 per minute, i.e. there are three events per minute, then λ=1/3, i.e. one event is expected on average to take place every 20 seconds.


We derive the mean as follows.

We will use integration by parts with u=−x and v=e−λx. We see that du=-1 and dv=−λe−λx.


We use the following formula for the variance.

Failed to parse (syntax error): {\displaystyle \operatorname{Var}(X) = \int^\infin_{0}x^2 \2 e^{-\2 x} dx-{2}}

We'll use integration by parts with u=−x2 and v=e−2x. From this we have du=−2x and dv=−2e-2x

We see that the integral is just E[X] which we solved for above.

External links[edit]