statistics

exponential distribution

The distribution of waiting times between events that happen at a constant rate; the law of radioactive decay.
10 related articles

Description

The exponential distribution models the time between events in a process where events occur continuously and independently at a constant average rate. It is the continuous analogue of the geometric distribution and is memoryless: the probability of waiting another minute does not depend on how long you have already waited. This makes it the natural model for radioactive decay, where each atom has a constant probability of decaying per unit time.

History

The distribution was first derived by the French mathematician Siméon Denis Poisson in the early 19th century, though it was later formalized in the context of radioactive decay by Ernest Rutherford and Hans Geiger in 1910.

How it works

If events occur at a rate λ per unit time, the probability density function is f(t) = λ * e^(-λt) for t ≥ 0. The mean waiting time is 1/λ, and the variance is 1/λ². The memoryless property means that P(T > t+s | T > s) = P(T > t), which is unique to the exponential distribution among continuous distributions.

In practice

It is used in reliability engineering to model the lifetime of components, in queueing theory for service times, and in finance for modeling the time between trades.

Fun fact

The exponential distribution is the only continuous distribution with the memoryless property, meaning a light bulb that has lasted 1000 hours is no more likely to fail in the next hour than a brand-new one.

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Related articles · 10

q-bio.MN2026-09-073 minarXiv:2609.90031
Precision That Doesn't Get in the Way of Change
An architect reinforces the foundation but leaves a door open for remodeling: that's how living systems balance reliability against renewal. A cell copies hereditary information with a fidelity any scribe would envy, yet it can also allow changes that help it survive under new conditions.
q-bio.OT2026-08-282 minarXiv:2608.90074
How Long Do You Think You'll Live? The Accuracy of Subjective Longevity Forecasts Among Japanese People
Every one of us has wondered at least once how many years we'll live. In 1996, nearly two and a half thousand Japanese people over sixty answered that question in a national survey. Their answers became a kind of mirror reflecting hopes, fears, and habits of thought. Twenty-eight years later, researchers compared those forecasts with actual dates of death and found that the mirror was warped, but not useless.
A Translator That Keeps Drifting Off, Then Gets Lost in a Monologue
A translator at a meeting keeps getting distracted by every rustle in the room, then suddenly dives into a long monologue by the speaker. That's exactly how the fast radio burst source FRB 20240114A behaves: in one hour, its pulses fire randomly, but over months they line up into a predictable drift.
q-bio.PE2026-08-131 minarXiv:2608.13370express
How Inheritance Division Shapes Survival
Two daughter cells receiving equal shares from the mother: that's the familiar picture of division. But what if one gets almost everything and the other just crumbs? Stochastic modeling shows that sometimes unequal division is more advantageous. Each cell stores a protective reserve that helps it weather hardships. During division, this reserve is distributed between daughters.
q-bio.QM2026-08-092 minarXiv:2608.08916express
A Protein Orchestra Without a Conductor
As you slowly turn the tuning knob of an old radio, the sound doesn't fade in gradually from the static: it bursts forth in a sudden jump, the moment the antenna hits resonance with the wave. Genes in a living cell behave much the same way.
q-bio.PE2026-07-131 minarXiv:2607.11011express
How a Tumor Builds Its Family Tree
Every tumor writes its own cosmology. Its cells are galaxies, and mutations are stellar flares marking the branches of a genealogical tree. The key to its history is the rhythm of accumulating genetic typos.
Machine Learning2026-04-252 minarXiv:2604.23368express
How a neural network reads a medical history
A doctor holds a stack of test results taken at different times. Each sheet on its own is just a snapshot of a single day, but what you want is a whole movie of how the disease unfolded. A neural network called Tempo learns to glue these scattered frames into a smooth reel.
q-bio.PE2026-01-262 minarXiv:2601.18703express
How an Epidemic Flows Through a City
An epidemic is like a forest fire: the flames race toward where there are more dry branches. Similarly, a virus spreads faster where people live in closer quarters. But old mathematical models described infection as an ink blot spreading evenly across paper.
Neural Network Denoises the Ocean and Predicts El Niño Two Years Ahead
Every spring, El Niño forecasts lose their edge, like a translator who gets distracted by every rustle in the room and turns a one-hour meeting into a two-hour one. Scientists call this the spring predictability barrier: in March and April, models that confidently predicted a warm phase in the Pacific back in January start making mistakes twice as often.
High Energy2025-10-081 minarXiv:2510.07002express
Artificial Intelligence Unravels the Mysteries of Cosmic Radio Bursts
Fast radio bursts are millisecond-long cosmic signals. Previously, searching for them was like blindly tuning a radio dial. The EDEN neural network sees the entire frequency spread and recognizes the burst pattern like a musician reads notes. On source FRB 20121102A, it found 5,927 bursts — three times more than before.