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Cumulative distribution function for the exponential distribution Cumulative distribution function for the normal distribution. In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable, or just distribution function of , evaluated at , is the probability that will take a value less than or equal to .
Computable Document Format (CDF) is an electronic document format [1] designed to allow authoring dynamically generated, interactive content. [2] CDF was created by Wolfram Research , and CDF files can be created using Mathematica . [ 3 ]
The CFBF file consists of a 512-Byte header record followed by a number of sectors whose size is defined in the header. The literature defines Sectors to be either 512 or 4096 bytes in length, although the format is potentially capable of supporting sectors ranging in size from 128-Bytes upwards in powers of 2 (128, 256, 512, 1024, etc.).
Compound Document Format (CDF) is a set of W3C candidate standards describing electronic compound document file formats that contains multiple formats, such as SVG, XHTML, SMIL and XForms. [1] The core standards are the Web Integration Compound Document and the Compound Document by Reference Framework (CDR).
In MATLAB we can use Empirical cumulative distribution function (cdf) plot; jmp from SAS, the CDF plot creates a plot of the empirical cumulative distribution function. Minitab, create an Empirical CDF; Mathwave, we can fit probability distribution to our data; Dataplot, we can plot Empirical CDF plot; Scipy, we can use scipy.stats.ecdf
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.The general form of its probability density function is [2] [3] = ().
The cumulative distribution function (cdf) of the half-logistic distribution is intimately related to the cdf of the logistic distribution. Formally, if F(k) is the cdf for the logistic distribution, then G(k) = 2F(k) − 1 is the cdf of a half-logistic distribution. Specifically,
The following are among the properties of log-concave distributions: If a density is log-concave, so is its cumulative distribution function (CDF). If a multivariate density is log-concave, so is the marginal density over any subset of variables. The sum of two independent log-concave random variables is log-concave. This follows from the fact ...