# mx.nd.random.pdf.dirichlet¶

## Description¶

Computes the value of the PDF of sample of Dirichlet distributions with parameter alpha.

The shape of alpha must match the leftmost subshape of sample. That is, sample can have the same shape as alpha, in which case the output contains one density per distribution, or sample can be a tensor of tensors with that shape, in which case the output is a tensor of densities such that the densities at index i in the output are given by the samples at index i in sample parameterized by the value of alpha at index i.

Example:

random_pdf_dirichlet(sample=[[1,2],[2,3],[3,4]], alpha=[2.5, 2.5]) =
[38.413498, 199.60245, 564.56085]

sample = [[[1, 2, 3], [10, 20, 30], [100, 200, 300]],
[[0.1, 0.2, 0.3], [0.01, 0.02, 0.03], [0.001, 0.002, 0.003]]]

random_pdf_dirichlet(sample=sample, alpha=[0.1, 0.4, 0.9]) =
[[2.3257459e-02, 5.8420084e-04, 1.4674458e-05],
[9.2589635e-01, 3.6860607e+01, 1.4674468e+03]]


## Arguments¶

Argument

Description

sample

NDArray-or-Symbol.

Samples from the distributions.

alpha

NDArray-or-Symbol.

Concentration parameters of the distributions.

is.log

boolean, optional, default=0.

If set, compute the density of the log-probability instead of the probability.

## Value¶

out The result mx.ndarray