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Rate Parameter (Lambda)

Definition

The single parameter of the Poisson distribution, equal to both the mean and the variance of the distribution. In forensic contexts lambda is the expected number of events (e.g., particles, rare alleles) per unit of time, area, or volume.

Symbol
Lambda
Distribution
Poisson
Equals
Both the mean and the variance
Forensic meaning
Expected event count per unit time, area, or volume

Common questions

Why is it notable that lambda equals both the mean and the variance of the Poisson distribution?+

It is a defining property unique to the Poisson model, and it lets an analyst estimate the expected spread of counts directly from a single observed average rate, without needing a separate variance estimate, though it also means the model fits poorly when real data are more dispersed than that.

What kind of forensic count data is typically modelled with a Poisson rate parameter?+

Rare, discrete events counted over a fixed interval fit the model well, such as the number of matching rare alleles observed, particle counts in a defined volume of trace material, or incident counts over a time period, provided the events occur independently.

Related terms

Likelihood Ratio (LR)
The ratio of two conditional probabilities: the probability of the observed evidence given the prosecution's hypothesis (same source), divided by the probability...
Parameters
The numerical constants that define the specific shape of a distribution within its family. The normal distribution has two parameters (mean and...
Probability Distribution
A function that assigns probabilities to each possible value (or range of values) of a random variable. It fully characterises the uncertainty...
Right Skew
A distribution is right-skewed when its tail extends further to the right than to the left. Many forensic measurement quantities (trace concentrations,...
Shape and Rate Parameters
The two parameters of the gamma distribution. The shape parameter controls how peaked or spread the distribution is; the rate parameter (sometimes...

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