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# This file is distributed under the same license as the mars package.
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#: ../../source/reference/tensor/generated/mars.tensor.random.dirichlet.rst:2
msgid "mars.tensor.random.dirichlet"
msgstr ""

#: mars.tensor.random.dirichlet:1 of
msgid "Draw samples from the Dirichlet distribution."
msgstr ""

#: mars.tensor.random.dirichlet:3 of
msgid ""
"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."
msgstr ""

#: mars.tensor.random.dirichlet of
msgid "Parameters"
msgstr ""

#: mars.tensor.random.dirichlet:8 of
msgid "Parameter of the distribution (k dimension for sample of dimension k)."
msgstr ""

#: mars.tensor.random.dirichlet:11 of
msgid ""
"Output shape.  If the given shape is, e.g., ``(m, n, k)``, then ``m * n *"
" k`` samples are drawn.  Default is None, in which case a single value is"
" returned."
msgstr ""

#: mars.tensor.random.dirichlet:15 of
msgid "Desired chunk size on each dimension"
msgstr ""

#: mars.tensor.random.dirichlet:17 of
msgid "Allocate the tensor on GPU if True, False as default"
msgstr ""

#: mars.tensor.random.dirichlet:19 of
msgid "Data-type of the returned tensor."
msgstr ""

#: mars.tensor.random.dirichlet of
msgid "Returns"
msgstr ""

#: mars.tensor.random.dirichlet:22 of
msgid "**samples** -- The drawn samples, of shape (size, alpha.ndim)."
msgstr ""

#: mars.tensor.random.dirichlet of
msgid "Return type"
msgstr ""

#: mars.tensor.random.dirichlet of
msgid "Raises"
msgstr ""

#: mars.tensor.random.dirichlet:25 of
msgid "If any value in alpha is less than or equal to zero"
msgstr ""

#: mars.tensor.random.dirichlet:28 of
msgid "Notes"
msgstr ""

#: mars.tensor.random.dirichlet:29 of
msgid "X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}"
msgstr ""

#: mars.tensor.random.dirichlet:31 of
msgid ""
"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 :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is "
"Dirichlet distributed."
msgstr ""

#: mars.tensor.random.dirichlet:38 of
msgid "References"
msgstr ""

#: mars.tensor.random.dirichlet:39 of
msgid ""
"David McKay, \"Information Theory, Inference and Learning Algorithms,\" "
"chapter 23, http://www.inference.phy.cam.ac.uk/mackay/"
msgstr ""

#: mars.tensor.random.dirichlet:42 of
msgid ""
"Wikipedia, \"Dirichlet distribution\", "
"http://en.wikipedia.org/wiki/Dirichlet_distribution"
msgstr ""

#: mars.tensor.random.dirichlet:46 of
msgid "Examples"
msgstr ""

#: mars.tensor.random.dirichlet:47 of
msgid ""
"Taking an example cited in Wikipedia, this distribution can be used if "
"one wanted to cut strings (each of initial length 1.0) into K pieces with"
" different lengths, where each piece had, on average, a designated "
"average length, but allowing some variation in the relative sizes of the "
"pieces."
msgstr ""

