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#: ../../source/reference/tensor/generated/mars.tensor.random.lognormal.rst:2
msgid "mars.tensor.random.lognormal"
msgstr ""

#: mars.tensor.random.lognormal:1 of
msgid "Draw samples from a log-normal distribution."
msgstr ""

#: mars.tensor.random.lognormal:3 of
msgid ""
"Draw samples from a log-normal distribution with specified mean, standard"
" deviation, and array shape.  Note that the mean and standard deviation "
"are not the values for the distribution itself, but of the underlying "
"normal distribution it is derived from."
msgstr ""

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

#: mars.tensor.random.lognormal:8 of
msgid "Mean value of the underlying normal distribution. Default is 0."
msgstr ""

#: mars.tensor.random.lognormal:10 of
msgid ""
"Standard deviation of the underlying normal distribution. Should be "
"greater than zero. Default is 1."
msgstr ""

#: mars.tensor.random.lognormal:13 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 ``mean`` and ``sigma`` are both scalars. Otherwise, "
"``np.broadcast(mean, sigma).size`` samples are drawn."
msgstr ""

#: mars.tensor.random.lognormal:18 of
msgid "Desired chunk size on each dimension"
msgstr ""

#: mars.tensor.random.lognormal:20 of
msgid "Allocate the tensor on GPU if True, False as default"
msgstr ""

#: mars.tensor.random.lognormal:22 of
msgid "Data-type of the returned tensor."
msgstr ""

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

#: mars.tensor.random.lognormal:25 of
msgid "**out** -- Drawn samples from the parameterized log-normal distribution."
msgstr ""

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

#: mars.tensor.random.lognormal:30 of
msgid ":func:`scipy.stats.lognorm`"
msgstr ""

#: mars.tensor.random.lognormal:31 of
msgid ""
"probability density function, distribution, cumulative density function, "
"etc."
msgstr ""

#: mars.tensor.random.lognormal:34 of
msgid "Notes"
msgstr ""

#: mars.tensor.random.lognormal:35 of
msgid ""
"A variable `x` has a log-normal distribution if `log(x)` is normally "
"distributed.  The probability density function for the log-normal "
"distribution is:"
msgstr ""

#: mars.tensor.random.lognormal:39 of
msgid ""
"p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}} "
"e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}"
msgstr ""

#: mars.tensor.random.lognormal:42 of
msgid ""
"where :math:`\\mu` is the mean and :math:`\\sigma` is the standard "
"deviation of the normally distributed logarithm of the variable. A log-"
"normal distribution results if a random variable is the *product* of a "
"large number of independent, identically-distributed variables in the "
"same way that a normal distribution results if the variable is the *sum* "
"of a large number of independent, identically-distributed variables."
msgstr ""

#: mars.tensor.random.lognormal:51 of
msgid "References"
msgstr ""

#: mars.tensor.random.lognormal:52 of
msgid ""
"Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions "
"across the Sciences: Keys and Clues,\" BioScience, Vol. 51, No. 5, May, "
"2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf"
msgstr ""

#: mars.tensor.random.lognormal:56 of
msgid ""
"Reiss, R.D. and Thomas, M., \"Statistical Analysis of Extreme Values,\" "
"Basel: Birkhauser Verlag, 2001, pp. 31-32."
msgstr ""

#: mars.tensor.random.lognormal:60 of
msgid "Examples"
msgstr ""

#: mars.tensor.random.lognormal:61 of
msgid "Draw samples from the distribution:"
msgstr ""

#: mars.tensor.random.lognormal:68 of
msgid ""
"Display the histogram of the samples, along with the probability density "
"function:"
msgstr ""

#: mars.tensor.random.lognormal:82 of
msgid ""
"Demonstrate that taking the products of random samples from a uniform "
"distribution can be fit well by a log-normal probability density "
"function."
msgstr ""

