In probability theory and directional statistics, a **wrapped probability distribution** is a continuous probability distribution that describes data points that lie on a unit *n*-sphere. In one dimension, a wrapped distribution will consist of points on the unit circle. If φ is a random variate in the interval (-∞,∞) with probability density function *p(φ)*, then *z = e ^{ i φ}* will be a circular variable distributed according to the wrapped distribution

Any probability density function (pdf) on the line can be "wrapped" around the circumference of a circle of unit radius.^{[1]} That is, the pdf of the wrapped variable

- in some interval of length

is

which is a periodic sum of period . The preferred interval is generally for which

In most situations, a process involving circular statistics produces angles () which lie in the interval from negative infinity to positive infinity, and are described by an "unwrapped" probability density function . However, a measurement will yield a "measured" angle which lies in some interval of length (for example ). In other words, a measurement cannot tell if the "true" angle has been measured or whether a "wrapped" angle has been measured where *a* is some unknown integer. That is:

If we wish to calculate the expected value of some function of the measured angle it will be:

We can express the integral as a sum of integrals over periods of (e.g. 0 to ):

Changing the variable of integration to and exchanging the order of integration and summation, we have

where is the pdf of the "wrapped" distribution and *a' * is another unknown integer (a'=a+k). It can be seen that the unknown integer *a' * introduces an ambiguity into the expectation value of . A particular instance of this problem is encountered when attempting to take the mean of a set of measured angles. If, instead of the measured angles, we introduce the parameter it is seen that *z* has an unambiguous relationship to the "true" angle since:

Calculating the expectation value of a function of *z* will yield unambiguous answers:

and it is for this reason that the *z* parameter is the preferred statistical variable to use in circular statistical analysis rather than the measured angles . This suggests, and it is shown below, that the wrapped distribution function may itself be expressed as a function of *z* so that:

where is defined such that . This concept can be extended to the multivariate context by an extension of the simple sum to a number of sums that cover all dimensions in the feature space:

where is the th Euclidean basis vector.

A fundamental wrapped distribution is the Dirac comb which is a wrapped Dirac delta function:

Using the delta function, a general wrapped distribution can be written

Exchanging the order of summation and integration, any wrapped distribution can be written as the convolution of the "unwrapped" distribution and a Dirac comb:

The Dirac comb may also be expressed as a sum of exponentials, so we may write:

again exchanging the order of summation and integration,

using the definition of , the characteristic function of , yields a Laurent series about zero for the wrapped distribution in terms of the characteristic function of the unwrapped distribution:^{[2]}

or

By analogy with linear distributions, the are referred to as the characteristic function of the wrapped distribution^{[2]} (or perhaps more accurately, the characteristic sequence). This is an instance of the Poisson summation formula and it can be seen that the Fourier coefficients of the Fourier series for the wrapped distribution are just the Fourier coefficients of the Fourier transform of the unwrapped distribution at integer values.

The moments of the wrapped distribution are defined as:

Expressing in terms of the characteristic function and exchanging the order of integration and summation yields:

From the theory of residues we have

where is the Kronecker delta function. It follows that the moments are simply equal to the characteristic function of the unwrapped distribution for integer arguments:

If *X* is a random variate drawn from a linear probability distribution *P*, then will be a circular variate distributed according to the wrapped *P* distribution, and will be the angular variate distributed according to the wrapped *P* distribution, with .

The information entropy of a circular distribution with probability density is defined as:^{[1]}

where is any interval of length . If both the probability density and its logarithm can be expressed as a Fourier series (or more generally, any integral transform on the circle) then the orthogonality property may be used to obtain a series representation for the entropy which may reduce to a closed form.

The moments of the distribution are the Fourier coefficients for the Fourier series expansion of the probability density:

If the logarithm of the probability density can also be expressed as a Fourier series:

where

Then, exchanging the order of integration and summation, the entropy may be written as:

Using the orthogonality of the Fourier basis, the integral may be reduced to:

For the particular case when the probability density is symmetric about the mean, and the logarithm may be written:

and

and, since normalization requires that , the entropy may be written: