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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.normal.rst:2
msgid "mars.tensor.random.normal"
msgstr ""

#: mars.tensor.random.normal:1 of
msgid "Draw random samples from a normal (Gaussian) distribution."
msgstr ""

#: mars.tensor.random.normal:3 of
msgid ""
"The probability density function of the normal distribution, first "
"derived by De Moivre and 200 years later by both Gauss and Laplace "
"independently [2]_, is often called the bell curve because of its "
"characteristic shape (see the example below)."
msgstr ""

#: mars.tensor.random.normal:8 of
msgid ""
"The normal distributions occurs often in nature.  For example, it "
"describes the commonly occurring distribution of samples influenced by a "
"large number of tiny, random disturbances, each with its own unique "
"distribution [2]_."
msgstr ""

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

#: mars.tensor.random.normal:13 of
msgid "Mean (\"centre\") of the distribution."
msgstr ""

#: mars.tensor.random.normal:15 of
msgid "Standard deviation (spread or \"width\") of the distribution."
msgstr ""

#: mars.tensor.random.normal:17 of
msgid ""
"Output shape.  If the given shape is, e.g., ``(m, n, k)``, then ``m * n *"
" k`` samples are drawn.  If size is ``None`` (default), a single value is"
" returned if ``loc`` and ``scale`` are both scalars. Otherwise, "
"``mt.broadcast(loc, scale).size`` samples are drawn."
msgstr ""

#: mars.tensor.random.normal:22 of
msgid "Desired chunk size on each dimension"
msgstr ""

#: mars.tensor.random.normal:24 of
msgid "Allocate the tensor on GPU if True, False as default"
msgstr ""

#: mars.tensor.random.normal:26 of
msgid "Data-type of the returned tensor."
msgstr ""

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

#: mars.tensor.random.normal:29 of
msgid "**out** -- Drawn samples from the parameterized normal distribution."
msgstr ""

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

#: mars.tensor.random.normal:34 of
msgid ":func:`scipy.stats.norm`"
msgstr ""

#: mars.tensor.random.normal:35 of
msgid ""
"probability density function, distribution or cumulative density "
"function, etc."
msgstr ""

#: mars.tensor.random.normal:38 of
msgid "Notes"
msgstr ""

#: mars.tensor.random.normal:39 of
msgid "The probability density for the Gaussian distribution is"
msgstr ""

#: mars.tensor.random.normal:41 of
msgid ""
"p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }} e^{ - \\frac{ (x - \\mu)^2 }"
" {2 \\sigma^2} },"
msgstr ""

#: mars.tensor.random.normal:44 of
msgid ""
"where :math:`\\mu` is the mean and :math:`\\sigma` the standard "
"deviation. The square of the standard deviation, :math:`\\sigma^2`, is "
"called the variance."
msgstr ""

#: mars.tensor.random.normal:48 of
msgid ""
"The function has its peak at the mean, and its \"spread\" increases with "
"the standard deviation (the function reaches 0.607 times its maximum at "
":math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that "
"`numpy.random.normal` is more likely to return samples lying close to the"
" mean, rather than those far away."
msgstr ""

#: mars.tensor.random.normal:55 of
msgid "References"
msgstr ""

#: mars.tensor.random.normal:56 of
msgid ""
"Wikipedia, \"Normal distribution\", "
"http://en.wikipedia.org/wiki/Normal_distribution"
msgstr ""

#: mars.tensor.random.normal:58 of
msgid ""
"P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random "
"Variables and Random Signal Principles\", 4th ed., 2001, pp. 51, 51, 125."
msgstr ""

#: mars.tensor.random.normal:63 of
msgid "Examples"
msgstr ""

#: mars.tensor.random.normal:64 of
msgid "Draw samples from the distribution:"
msgstr ""

#: mars.tensor.random.normal:71 of
msgid "Verify the mean and the variance:"
msgstr ""

#: mars.tensor.random.normal:79 of
msgid ""
"Display the histogram of the samples, along with the probability density "
"function:"
msgstr ""

