naginterfaces.library.rand.dist_vonmises¶
- naginterfaces.library.rand.dist_vonmises(n, vk, statecomm)[source]¶
dist_vonmises
generates a vector of pseudorandom numbers from a von Mises distribution with concentration parameter .For full information please refer to the NAG Library document for g05sr
https://support.nag.com/numeric/nl/nagdoc_30.3/flhtml/g05/g05srf.html
- Parameters
- nint
, the number of pseudorandom numbers to be generated.
- vkfloat
, the concentration parameter of the required von Mises distribution.
- statecommdict, RNG communication object, modified in place
RNG communication structure.
This argument must have been initialized by a prior call to
init_repeat()
orinit_nonrepeat()
.
- Returns
- xfloat, ndarray, shape
The pseudorandom numbers from the specified von Mises distribution.
- Raises
- NagValueError
- (errno )
On entry, .
Constraint: .
- (errno )
On entry, or too large: .
- (errno )
On entry, [‘state’] vector has been corrupted or not initialized.
- Notes
The von Mises distribution is a symmetric distribution used in the analysis of circular data. The PDF (probability density function) of this distribution on the circle with mean direction and concentration parameter , can be written as:
where is reduced modulo so that and . For very small the distribution is almost the uniform distribution, whereas for all the probability is concentrated at one point.
The variates, , are generated using an envelope rejection method with a wrapped Cauchy target distribution as proposed by Best and Fisher (1979) and described by Dagpunar (1988).
One of the initialization functions
init_repeat()
(for a repeatable sequence if computed sequentially) orinit_nonrepeat()
(for a non-repeatable sequence) must be called prior to the first call todist_vonmises
.
- References
Best, D J and Fisher, N I, 1979, Efficient simulation of the von Mises distribution, Appl. Statist. (28), 152–157
Dagpunar, J, 1988, Principles of Random Variate Generation, Oxford University Press
Mardia, K V, 1972, Statistics of Directional Data, Academic Press