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/

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