Linked from

The 16 pages that link to Exponential distribution, each with the reason it gives.

• Random variableBroader topic: It models waiting times between events in a Poisson process.

• Probability distributionBroader topic: Its memoryless property makes it a standard model for waiting times.

• Probability density functionBroader topic: It models waiting times with a simple, explicitly integrable density.

• Mean free pathRelated: In a uniform medium, free-flight distances commonly follow this distribution.

• Poisson distributionRelated: It describes the gaps between events whose counts follow this distribution.

• Queueing theoryRelated: It models memoryless service times and underlies many tractable queueing models.

• Cumulative distribution functionBroader topic: Its CDF rises from zero toward one as one minus an exponential tail.

• Moment-generating functionBroader topic: Its moment-generating function is a simple example with a finite interval of existence.

• Power lawCompared with: Its characteristic scale separates it from the heavy tails associated with many power-law models.

• Geometric distributionCompared with: It is the continuous-time counterpart of geometric waiting under a constant event rate.

• Poisson processRelated: Successive waiting times between events follow this distribution.

• Method of momentsRelated: Its theoretical mean gives a direct moment equation for the rate.

• Gamma distributionBroader topic: It is the gamma distribution’s shape-one special case.

• Log-normal distributionCompared with: It is less flexible in shape and has a lighter tail than a log-normal distribution.

• Lehmann–Scheffé theoremRelated: The sample sum is complete and sufficient for the rate in the independent exponential model.

• Continuous uniform distributionCompared with: Its density declines across the support instead of remaining constant.