UniformSumDistribution

UniformSumDistribution[n]
表示 n 个从 均匀分布的随机变量的总和的分布.

UniformSumDistribution[n,{min,max}]
表示 n 个从 minmax 均匀分布的随机变量的总和的分布.

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背景
背景

  • UniformSumDistribution[n,{min,max}] represents a statistical distribution defined over the interval from min to max and parametrized by the positive integer n. The overall shape of the probability distribution function (PDF) of a uniform sum distribution varies significantly depending on n and can be uniform, triangular, or unimodal with maximum at when , , or , respectively. The one-argument form UniformSumDistribution[n] is equivalent to UniformSumDistribution[n,{0,1}] and is sometimes called the standardized uniform sum distribution. The uniform sum distribution is also known as the IrwinHall distribution.
  • The uniform sum distribution UniformSumDistribution[n] is defined to be the sum of n statistically independent, uniformly distributed random variables , i.e. is equivalent to saying that , where XiUniformDistribution[] for all . The two-argument form UniformSumDistribution[n,{min,max}] has the same meaning, with the exception that XiUniformDistribution[{min,max}]. One important application of the uniform sum distribution is in computing, where the standardized uniform sum distribution with has been used historically to generate standard normal variables. Despite this fact, it should be noted that the UniformSumDistribution[n] is not totally smooth (as is NormalDistribution) because its PDF becomes nonsmooth after taking derivatives. UniformSumDistribution also arises in a number of engineering applications and is particularly useful when modeling the life cycles of various manufactured goods.
  • RandomVariate can be used to give one or more machine- or arbitrary-precision (the latter via the WorkingPrecision option) pseudorandom variates from a uniform sum distribution. Distributed[x,UniformSumDistribution[n,{min,max}]], written more concisely as , can be used to assert that a random variable x is distributed according to a uniform sum distribution. Such an assertion can then be used in functions such as Probability, NProbability, Expectation, and NExpectation.
  • The probability distribution and cumulative density functions may be given using PDF[UniformSumDistribution[n,{min,max}],x] and CDF[UniformSumDistribution[n,{min,max}],x]. The mean, median, variance, raw moments, and central moments may be computed using Mean, Median, Variance, Moment, and CentralMoment, respectively.
  • DistributionFitTest can be used to test if a given dataset is consistent with a uniform sum distribution, EstimatedDistribution to estimate a uniform sum parametric distribution from given data, and FindDistributionParameters to fit data to a uniform sum distribution. ProbabilityPlot can be used to generate a plot of the CDF of given data against the CDF of a symbolic uniform sum distribution, and QuantilePlot to generate a plot of the quantiles of given data against the quantiles of a symbolic uniform sum distribution.
  • TransformedDistribution can be used to represent a transformed uniform sum distribution, CensoredDistribution to represent the distribution of values censored between upper and lower values, and TruncatedDistribution to represent the distribution of values truncated between upper and lower values. CopulaDistribution can be used to build higher-dimensional distributions that contain a uniform sum distribution, and ProductDistribution can be used to compute a joint distribution with independent component distributions involving uniform sum distributions.
  • UniformSumDistribution is closely related to a number of other distributions. For example, the PDF of a uniform sum distribution is precisely UniformDistribution and TriangularDistribution for and , respectively, and appears visually similar to the PDF of NormalDistribution for larger values . (This similarity is due to the fact that UniformSumDistribution[n] tends to NormalDistribution[μ,σ] where μ and σ denote the mean and standard deviation, respectively, of UniformSumDistribution[n].) UniformSumDistribution is also closely related to BatesDistribution, which represents the mean of statistically independent, uniformly distributed random variables (rather than their sum).
2010年引入
(8.0)