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MessagePosté le: Jeu 1 Sep - 09:27 (2016)    Sujet du message: Convert Pdf To Cdf Matlab Download Répondre en citant




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Output Argumentscollapse ally Cumulative distribution functionarray Cumulative distribution function of the specified probability distribution, returned as an array. Use the cdf function, and specify a standard normal distribution using the same parameter values for and .y2 = cdf('Normal',x,mu,sigma) y2 = 0.0228 0.1587 0.5000 0.8413 0.9772 The cdf values are the same as those computed using the probability distribution object.Compute the Poisson Distribution cdfOpen This Example Create a Poisson distribution object with the rate parameter, , equal to 2.lambda = 2; pd = makedist('Poisson',lambda); Define the input vector x to contain the values at which to calculate the cdf.x = [0,1,2,3,4]; Compute the cdf values for the Poisson distribution at the values in x.y = cdf(pd,x) y = 0.1353 0.4060 0.6767 0.8571 0.9473 Each value in y corresponds to a value in the input vector x. The user has complete control over how the data values are stored in the CDF (within the confines of the variable array structure) depending on how the user views the data. Note: To improve performance when working with large data files, use the 'ConvertEpochToDatenum' and 'CombineRecords' options.Note: To improve performance, turn off the file validation which the CDF library does by default when opening files. The multi-file format, on the other hand, clearly delimits the data from the metadata and is organized in a consistent fashion within the files. But if you are analyzing data files that were created with early CDF releases or contain rVariables for some reason, you'll need to use rVariables. A README file is also required. All Products Statistics and Machine Learning Toolbox Examples Functions and Other Reference Release Notes PDF Documentation Probability Distributions Discrete Distributions Binomial Distribution Statistics and Machine Learning Toolbox Probability Distributions Discrete Distributions Multinomial Distribution Statistics and Machine Learning Toolbox Probability Distributions Discrete Distributions Negative Binomial Distribution Statistics and Machine Learning Toolbox Probability Distributions Discrete Distributions Poisson Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Beta Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Birnbaum-Saunders Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Burr Type XII Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Exponential Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Extreme Value Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Gamma Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Generalized Extreme Value Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Generalized Pareto Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Half-Normal Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Inverse Gaussian Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Kernel Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Logistic Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Loglogistic Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Lognormal Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Nakagami Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Normal Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Piecewise Linear Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Rayleigh Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Rician Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Stable Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions t Location-Scale Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Triangular Distribution Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Uniform Distribution (Continuous) Statistics and Machine Learning Toolbox Probability Distributions Continuous Distributions Weibull Distribution Statistics and Machine Learning Toolbox Functions pdf On this page Syntax Description Examples Compute the Normal Distribution pdf Compute the Poisson Distribution pdf Input Arguments 'name' x A B C D pd Output Arguments y See Also .

Data Types: single doublepd Probability distributionprobability distribution object Probability distribution, specified as a probability distribution object created using one of the following. What's the difference between a single-file CDF and multi-file CDF? The CDF library gives the user option to choose from one of two file formats: single-file and multi-file. If you specify distribution parameters A, B, C, or D, then y is the same size as x, A, B, C, and D after any necessary scalar expansion.If you specify a probability distribution object, pd, then y has the same dimensions as x. Data values are organized in whatever way makes sense to the user. They are: - Compression: Compression is not allowed for the CDF or any of its variables. A standalone tool: CDFValidate (mentioned in Item 3: What CDF utility programs are available? in this page) is written to assist in validating a given CDF(s). 23. Subject: [area]: [synopsis] [replace with actual AREA and SYNOPSIS] VERSION: [CDF library/toolkit version (e.g., CDF Version 2.5.1). If x is a scalar value, and if you specify distribution parameters A, B, C, or D as arrays, then cdf expands x into a constant array of the same size as the parameters.If x is an array, and if you specify distribution parameters A, B, C, or D as arrays, then x, A, B, C, and D must all be the same size.

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