numpy.random.mtrand.dirichlet¶

numpy.random.mtrand.dirichlet(alpha, size=None)¶

Draw samples from the Dirichlet distribution.

Draw size samples of dimension k from a Dirichlet distribution. A Dirichlet-distributed random variable can be seen as a multivariate generalization of a Beta distribution. Dirichlet pdf is the conjugate prior of a multinomial in Bayesian inference.

Parameters :

alpha : array

Parameter of the distribution (k dimension for sample of dimension k).

size : array

Number of samples to draw.

Notes

$X \approx \prod_{i=1}^{k}{x^{\alpha_i-1}_i}$

Uses the following property for computation: for each dimension, draw a random sample y_i from a standard gamma generator of shape alpha_i, then $X = \frac{1}{\sum_{i=1}^k{y_i}} (y_1, \ldots, y_n)$ is Dirichlet distributed.

References

[R224]

David McKay, “Information Theory, Inference and Learning Algorithms,” chapter 23, http://www.inference.phy.cam.ac.uk/mackay/

Navigation

numpy.random.mtrand.dirichlet¶

This Page

Quick search

Navigation