# SOME DESCRIPTIVE TITLE.
# Copyright (C) 1999-2020, The Alibaba Group Holding Ltd.
# This file is distributed under the same license as the mars package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2020.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: mars 0.5.0a2\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2020-06-16 16:57+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.7.0\n"

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

#: mars.tensor.random.binomial:1 of
msgid "Draw samples from a binomial distribution."
msgstr ""

#: mars.tensor.random.binomial:3 of
msgid ""
"Samples are drawn from a binomial distribution with specified parameters,"
" n trials and p probability of success where n an integer >= 0 and p is "
"in the interval [0,1]. (n may be input as a float, but it is truncated to"
" an integer in use)"
msgstr ""

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

#: mars.tensor.random.binomial:8 of
msgid ""
"Parameter of the distribution, >= 0. Floats are also accepted, but they "
"will be truncated to integers."
msgstr ""

#: mars.tensor.random.binomial:11 of
msgid "Parameter of the distribution, >= 0 and <=1."
msgstr ""

#: mars.tensor.random.binomial: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 ``n`` and ``p`` are both scalars. Otherwise, "
"``mt.broadcast(n, p).size`` samples are drawn."
msgstr ""

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

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

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

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

#: mars.tensor.random.binomial:25 of
msgid ""
"**out** -- Drawn samples from the parameterized binomial distribution, "
"where each sample is equal to the number of successes over the n trials."
msgstr ""

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

#: mars.tensor.random.binomial:31 of
msgid ":func:`scipy.stats.binom`"
msgstr ""

#: mars.tensor.random.binomial:32 of
msgid ""
"probability density function, distribution or cumulative density "
"function, etc."
msgstr ""

#: mars.tensor.random.binomial:35 of
msgid "Notes"
msgstr ""

#: mars.tensor.random.binomial:36 of
msgid "The probability density for the binomial distribution is"
msgstr ""

#: mars.tensor.random.binomial:38 of
msgid "P(N) = \\binom{n}{N}p^N(1-p)^{n-N},"
msgstr ""

#: mars.tensor.random.binomial:40 of
msgid ""
"where :math:`n` is the number of trials, :math:`p` is the probability of "
"success, and :math:`N` is the number of successes."
msgstr ""

#: mars.tensor.random.binomial:43 of
msgid ""
"When estimating the standard error of a proportion in a population by "
"using a random sample, the normal distribution works well unless the "
"product p*n <=5, where p = population proportion estimate, and n = number"
" of samples, in which case the binomial distribution is used instead. For"
" example, a sample of 15 people shows 4 who are left handed, and 11 who "
"are right handed. Then p = 4/15 = 27%. 0.27*15 = 4, so the binomial "
"distribution should be used in this case."
msgstr ""

#: mars.tensor.random.binomial:52 of
msgid "References"
msgstr ""

#: mars.tensor.random.binomial:53 of
msgid ""
"Dalgaard, Peter, \"Introductory Statistics with R\", Springer-Verlag, "
"2002."
msgstr ""

#: mars.tensor.random.binomial:55 of
msgid ""
"Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill, Fifth "
"Edition, 2002."
msgstr ""

#: mars.tensor.random.binomial:57 of
msgid ""
"Lentner, Marvin, \"Elementary Applied Statistics\", Bogden and Quigley, "
"1972."
msgstr ""

#: mars.tensor.random.binomial:59 of
msgid ""
"Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A Wolfram "
"Web Resource. http://mathworld.wolfram.com/BinomialDistribution.html"
msgstr ""

#: mars.tensor.random.binomial:62 of
msgid ""
"Wikipedia, \"Binomial distribution\", "
"http://en.wikipedia.org/wiki/Binomial_distribution"
msgstr ""

#: mars.tensor.random.binomial:66 of
msgid "Examples"
msgstr ""

#: mars.tensor.random.binomial:67 of
msgid "Draw samples from the distribution:"
msgstr ""

#: mars.tensor.random.binomial:75 of
msgid ""
"A real world example. A company drills 9 wild-cat oil exploration wells, "
"each with an estimated probability of success of 0.1. All nine wells "
"fail. What is the probability of that happening?"
msgstr ""

#: mars.tensor.random.binomial:79 of
msgid ""
"Let's do 20,000 trials of the model, and count the number that generate "
"zero positive results."
msgstr ""

