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BetaDistribution


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 -- statistics: BetaDistribution

     Beta probability distribution object.

     A ‘BetaDistribution’ object consists of parameters, a model description,
     and sample data for a beta probability distribution.

     The beta distribution is a family of continuous probability distributions
     defined on the interval [0, 1] in terms of two positive parameters, denoted
     by alpha (A) and beta (B), that appear as exponents of the variable and its
     complement to 1, respectively, and control the shape of the distribution.

     There are several ways to create a ‘BetaDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor BetaDistribution (A, B) to create a beta
          distribution with fixed parameter values A and B.
        • Use the static method BetaDistribution.fit (X, ALPHA, FREQ, OPTIONS)
          to fit a distribution to the data in X using the same input arguments
          as the ‘betafit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the beta distribution can be found at
     <https://en.wikipedia.org/wiki/Beta_distribution>

     See also: fitdist, makedist, betacdf, betainv, betapdf, betarnd, betafit,
     betalike, betastat.


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Beta probability distribution object.



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BinomialDistribution


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 -- statistics: BinomialDistribution

     Binomial probability distribution object.

     A ‘BinomialDistribution’ object consists of parameters, a model
     description, and sample data for a binomial probability distribution.

     The binomial distribution is a discrete probability distribution that
     models the number of successes in a sequence of N independent trials, each
     with a probability of success P.

     There are several ways to create a ‘BinomialDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor BinomialDistribution (N, P) to create a binomial
          distribution with fixed parameter values N and P.
        • Use the static method BinomialDistribution.fit (X, NTRIALS, ALPHA) to
          fit a distribution to the data in X using the same input arguments as
          the ‘binofit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the binomial distribution can be found at
     <https://en.wikipedia.org/wiki/Binomial_distribution>

     See also: fitdist, makedist, binocdf, binoinv, binopdf, binornd, binofit,
     binolike, binostat.


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Binomial probability distribution object.



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BirnbaumSaundersDistribution


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 -- statistics: BirnbaumSaundersDistribution

     Birnbaum-Saunders probability distribution object.

     A ‘BirnbaumSaundersDistribution’ object consists of parameters, a model
     description, and sample data for a Birnbaum-Saunders probability
     distribution.

     The Birnbaum-Saunders distribution is a continuous probability distribution
     that models the time to failure of materials subjected to cyclic loading.
     It is defined by scale parameter BETA and shape parameter GAMMA.

     There are several ways to create a ‘BirnbaumSaundersDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor BirnbaumSaundersDistribution (BETA, GAMMA) to
          create a Birnbaum-Saunders distribution with fixed parameter values
          BETA and GAMMA.
        • Use the static method BirnbaumSaundersDistribution.fit (X, ALPHA,
          CENSOR, FREQ, OPTIONS) to fit a distribution to the data in X using
          the same input arguments as the ‘bisafit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Birnbaum-Saunders distribution can be found
     at <https://en.wikipedia.org/wiki/Birnbaum%E2%80%93Saunders_distribution>

     See also: fitdist, makedist, bisacdf, bisainv, bisapdf, bisarnd, bisafit,
     bisalike, bisastat.


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Birnbaum-Saunders probability distribution object.



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BurrDistribution


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 -- statistics: BurrDistribution

     Burr probability distribution object.

     A ‘BurrDistribution’ object consists of parameters, a model description,
     and sample data for a Burr probability distribution.

     The Burr distribution is a continuous probability distribution that models
     a non-negative random variable, commonly used to model household income.
     It is defined by a scale parameter ALPHA and two shape parameters C and K.

     There are several ways to create a ‘BurrDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor BurrDistribution (ALPHA, C, K) to create a Burr
          distribution with fixed parameter values ALPHA, C, and K.
        • Use the static method BurrDistribution.fit (X, ALPHA, CENSOR, FREQ,
          OPTIONS) to fit a distribution to the data in X using the same input
          arguments as the ‘burrfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Burr distribution can be found at
     <https://en.wikipedia.org/wiki/Burr_distribution>

     See also: fitdist, makedist, burrcdf, burrinv, burrpdf, burrrnd, burrfit,
     burrlike, burrstat.


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Burr probability distribution object.



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ExponentialDistribution


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 -- statistics: ExponentialDistribution

     Exponential probability distribution object.

     A ‘ExponentialDistribution’ object consists of parameters, a model
     description, and sample data for a exponential probability distribution.

     The exponential distribution is a continuous probability distribution with
     mean parameter MU that models the time between events in a Poisson process.

     There are several ways to create a ‘ExponentialDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor ExponentialDistribution (MU) to create a
          exponential distribution with fixed parameter value MU.
        • Use the static method ExponentialDistribution.fit (X, ALPHA, CENSOR,
          FREQ, OPTIONS) to fit a distribution to the data in X using the same
          input arguments as the ‘expfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the exponential distribution can be found at
     <https://en.wikipedia.org/wiki/Exponential_distribution>

     See also: fitdist, makedist, expcdf, expinv, exppdf, exprnd, expfit,
     explike, expstat.


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Exponential probability distribution object.



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ExtremeValueDistribution


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 -- statistics: ExtremeValueDistribution

     Extreme value probability distribution object.

     A ‘ExtremeValueDistribution’ object consists of parameters, a model
     description, and sample data for an extreme value probability distribution.

     The extreme value distribution is also known as the Gumbel distribution for
     maxima, and it is a limiting distribution for the maximum of a large number
     of samples from a continuous distribution.  It is defined by location
     parameter MU and scale parameter SIGMA.

     There are several ways to create a ‘ExtremeValueDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using the
          ‘makedist’ function.
        • Use the constructor ExtremeValueDistribution (MU, SIGMA) to create an
          extreme value distribution with specified parameter values.
        • Use the static method ExtremeValueDistribution.fit (X, ALPHA, CENSOR,
          FREQ, OPTIONS) to fit a distribution to the data in X using the same
          input arguments as the ‘evfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the constructor and the
     aforementioned static method.

     Further information about the Gumbel distribution can be found at
     <https://en.wikipedia.org/wiki/Gumbel_distribution>

     See also: fitdist, makedist, evcdf, evinv, evpdf, evrnd, evfit, evlike,
     evstat.


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Extreme value probability distribution object.



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GammaDistribution


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 -- statistics: GammaDistribution

     Gamma probability distribution object.

     A ‘GammaDistribution’ object consists of parameters, a model description,
     and sample data for a gamma probability distribution.

     The gamma distribution is a continuous probability distribution that models
     the time to failure of a process.  It is defined by shape parameter A and
     scale parameter B.

     There are several ways to create a ‘GammaDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor GammaDistribution (A, B) to create a gamma
          distribution with fixed parameter values A and B.
        • Use the static method GammaDistribution.fit (X, ALPHA, CENSOR, FREQ,
          OPTIONS) to fit a distribution to the data in X using the same input
          arguments as the ‘gamfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the gamma distribution can be found at
     <https://en.wikipedia.org/wiki/Gamma_distribution>

     See also: fitdist, makedist, gamcdf, gaminv, gampdf, gamrnd, gamfit,
     gamlike, gamstat.


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Gamma probability distribution object.



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GeneralizedExtremeValueDistribution


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 -- statistics: GeneralizedExtremeValueDistribution

     Generalized extreme value probability distribution object.

     A ‘GeneralizedExtremeValueDistribution’ object consists of parameters, a
     model description, and sample data for a generalized extreme value
     probability distribution.

     The generalized extreme value distribution is a continuous probability
     distribution that models extreme values.  It is defined by shape parameter
     K, scale parameter SIGMA, and location parameter MU.

     There are several ways to create a ‘GeneralizedExtremeValueDistribution’
     object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor GeneralizedExtremeValueDistribution (K, SIGMA, MU)
          to create a generalized extreme value distribution with fixed
          parameter values K, SIGMA, and MU.
        • Use the static method GeneralizedExtremeValueDistribution.fit (X,
          ALPHA, FREQ, OPTIONS) to fit a distribution to the data in X using the
          same input arguments as the ‘gevfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the generalized extreme value distribution can be
     found at
     <https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution>

     See also: fitdist, makedist, gevcdf, gevinv, gevpdf, gevrnd, gevfit,
     gevlike, gevstat.


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Generalized extreme value probability distribution object.



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GeneralizedParetoDistribution


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 -- statistics: GeneralizedParetoDistribution

     Generalized Pareto probability distribution object.

     A ‘GeneralizedParetoDistribution’ object consists of parameters, a model
     description, and sample data for a Generalized Pareto probability
     distribution.

     The Generalized Pareto distribution is a continuous probability
     distribution that models the tail behavior of other distributions, commonly
     used for extreme value analysis.  It is defined by shape parameter K, scale
     parameter SIGMA, and location parameter THETA.

     There are several ways to create a ‘GeneralizedParetoDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor GeneralizedParetoDistribution (K, SIGMA, THETA) to
          create a Generalized Pareto distribution with fixed parameter values
          K, SIGMA, and THETA.
        • Use the static method GeneralizedParetoDistribution.fit (X, THETA,
          ALPHA, FREQ, OPTIONS) to fit a distribution to the data in X using the
          same input arguments as the ‘gpfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Generalized Pareto distribution can be found
     at <https://en.wikipedia.org/wiki/Generalized_Pareto_distribution>

     See also: fitdist, makedist, gpcdf, gpinv, gppdf, gprnd, gpfit, gplike,
     gpstat.


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Generalized Pareto probability distribution object.



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HalfNormalDistribution


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 -- statistics: HalfNormalDistribution

     Half-normal probability distribution object.

     A ‘HalfNormalDistribution’ object consists of parameters, a model
     description, and sample data for a half-normal probability distribution.

     The half-normal distribution is a continuous probability distribution that
     models the time to failure of materials subjected to cyclic loading.  It is
     defined by location parameter MU and scale parameter SIGMA.

     There are several ways to create a ‘HalfNormalDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor HalfNormalDistribution (MU, SIGMA) to create a
          half-normal distribution with fixed parameter values MU and SIGMA.
        • Use the static method HalfNormalDistribution.fit (X, MU, FREQ) to fit
          a distribution to the data in X using the same input arguments as the
          ‘hnfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the half-normal distribution can be found at
     <https://en.wikipedia.org/wiki/Half-normal_distribution>

     See also: fitdist, makedist, hncdf, hninv, hnpdf, hnrnd, hnfit, hnlike,
     hnstat.


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Half-normal probability distribution object.



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InverseGaussianDistribution


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 -- statistics: InverseGaussianDistribution

     Inverse Gaussian probability distribution object.

     A ‘InverseGaussianDistribution’ object consists of parameters, a model
     description, and sample data for a Inverse Gaussian probability
     distribution.

     The Inverse Gaussian distribution is a continuous probability distribution,
     which is often used to model non-negative positively skewed data.  Is is
     defined by mean parameter MU and shape parameter LAMBDA.

     There are several ways to create a ‘InverseGaussianDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor InverseGaussianDistribution (MU, LAMBDA) to create
          a Inverse Gaussian distribution with fixed parameter values MU and
          LAMBDA.
        • Use the static method InverseGaussianDistribution.fit (X, ALPHA,
          CENSOR, FREQ, OPTIONS) to fit a distribution to the data in X using
          the same input arguments as the ‘invgfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Inverse Gaussian distribution can be found at
     <https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution>

     See also: fitdist, makedist, invgcdf, invginv, invgpdf, invgrnd, invgfit,
     invglike, invgstat.


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Inverse Gaussian probability distribution object.



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LogisticDistribution


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 -- statistics: LogisticDistribution

     Logistic probability distribution object.

     A ‘LogisticDistribution’ object consists of parameters, a model
     description, and sample data for a logistic probability distribution.

     The logistic distribution is a continuous probability distribution, which
     is commonly used in logistic regression and feedforward neural networks.
     It is defined by location parameter MU and scale parameter SIGMA.

     There are several ways to create a ‘LogisticDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor LogisticDistribution (MU, SIGMA) to create a
          logistic distribution with fixed parameter values MU and SIGMA.
        • Use the static method LogisticDistribution.fit (X, ALPHA, CENSOR,
          FREQ, OPTIONS) to fit a distribution to the data in X using the same
          input arguments as the ‘logifit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the logistic distribution can be found at
     <https://en.wikipedia.org/wiki/Logistic_distribution>

     See also: fitdist, makedist, logicdf, logiinv, logipdf, logirnd, logifit,
     logilike, logistat.


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Logistic probability distribution object.



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LoglogisticDistribution


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 -- statistics: LoglogisticDistribution

     Log-logistic probability distribution object.

     A ‘LoglogisticDistribution’ object consists of parameters, a model
     description, and sample data for a log-logistic probability distribution.

     The log-logistic distribution is a continuous probability distribution that
     models non-negative random variables whose logarithm follows the logistic
     distribution.  It is defined by location parameter MU and scale parameter
     SIGMA.

     There are several ways to create a ‘LoglogisticDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor LoglogisticDistribution (MU, SIGMA) to create a
          log-logistic distribution with fixed parameter values MU and SIGMA.
        • Use the static method LoglogisticDistribution.fit (X, CENSOR, FREQ,
          OPTIONS) to fit a distribution to the data in X using the same input
          arguments as the ‘loglfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the log-logistic distribution can be found at
     <https://en.wikipedia.org/wiki/Log-logistic_distribution>

     See also: fitdist, makedist, loglcdf, loglinv, loglpdf, loglrnd, loglfit,
     logllike, loglstat.


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Log-logistic probability distribution object.



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LognormalDistribution


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 -- statistics: LognormalDistribution

     Lognormal probability distribution object.

     A ‘LognormalDistribution’ object consists of parameters, a model
     description, and sample data for a lognormal probability distribution.

     The lognormal distribution is a continuous probability distribution whose
     logarithm is normally distributed.  It is defined by mean parameter MU and
     standard deviation parameter SIGMA of the logarithmic values.

     There are several ways to create a ‘LognormalDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor LognormalDistribution (MU, SIGMA) to create a
          lognormal distribution with fixed parameter values MU and SIGMA.
        • Use the static method LognormalDistribution.fit (X, CENSOR, FREQ,
          OPTIONS) to fit a distribution to the data in X using the same input
          arguments as the ‘lognfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the lognormal distribution can be found at
     <https://en.wikipedia.org/wiki/Log-normal_distribution>

     See also: fitdist, makedist, logncdf, logninv, lognpdf, lognrnd, lognfit,
     lognlike, lognstat.


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Lognormal probability distribution object.



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LoguniformDistribution


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 -- statistics: LoguniformDistribution

     Log-uniform probability distribution object.

     A ‘LoguniformDistribution’ object consists of parameters and a model
     description for a log-uniform probability distribution.

     The log-uniform distribution is a continuous probability distribution that
     is constant between locations LOWER and UPPER on a logarithmic scale.

     There are several ways to create a ‘LoguniformDistribution’ object.

        • Create a distribution with specified parameter values using the
          ‘makedist’ function.
        • Use the constructor LoguniformDistribution (LOWER, UPPER) to create a
          log-uniform distribution with specified parameter values LOWER and
          UPPER.

     It is highly recommended to use ‘makedist’ function to create probability
     distribution objects, instead of the class constructor.

     Further information about the log-uniform distribution can be found at
     <https://en.wikipedia.org/wiki/Reciprocal_distribution>

     See also: makedist.


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Log-uniform probability distribution object.



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MultinomialDistribution


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 -- statistics: MultinomialDistribution

     Multinomial probability distribution object.

     A ‘MultinomialDistribution’ object consists of parameters, a model
     description, and sample data for a multinomial probability distribution.

     The multinomial distribution is a discrete probability distribution that
     models the outcomes of n independent trials of a k-category system, where
     each trial has a probability of falling into each category.  It is defined
     by the vector of probabilities for each outcome.

     There are several ways to create a ‘MultinomialDistribution’ object.

        • Create a distribution with specified parameter values using the
          ‘makedist’ function.
        • Use the constructor MultinomialDistribution (PROBABILITIES) to create
          a multinomial distribution with specified parameter values.

     It is highly recommended to use the ‘makedist’ function to create
     probability distribution objects, instead of the constructor.

     Further information about the multinomial distribution can be found at
     <https://en.wikipedia.org/wiki/Multinomial_distribution>

     See also: makedist, mnpdf, mnrnd.


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Multinomial probability distribution object.



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NakagamiDistribution


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 -- statistics: NakagamiDistribution

     Nakagami probability distribution object.

     A ‘NakagamiDistribution’ object consists of parameters, a model
     description, and sample data for a Nakagami probability distribution.

     The Nakagami distribution is a continuous probability distribution that
     models the amplitude of received signals after maximum ratio diversity
     combining.  It is defined by shape parameter MU and spread parameter OMEGA.

     There are several ways to create a ‘NakagamiDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor NakagamiDistribution (MU, OMEGA) to create a
          Nakagami distribution with fixed parameter values MU and OMEGA.
        • Use the static method NakagamiDistribution.fit (X, CENSOR, FREQ,
          OPTIONS) to fit a distribution to the data in X using the same input
          arguments as the ‘nakafit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Nakagami distribution can be found at
     <https://en.wikipedia.org/wiki/Nakagami_distribution>

     See also: fitdist, makedist, nakacdf, nakainv, nakapdf, nakarnd, nakafit,
     nakalike, nakastat.


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Nakagami probability distribution object.



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NegativeBinomialDistribution


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 -- statistics: NegativeBinomialDistribution

     Negative binomial probability distribution object.

     A ‘NegativeBinomialDistribution’ object consists of parameters, a model
     description, and sample data for a negative binomial probability
     distribution.

     The negative binomial distribution is a discrete probability distribution
     that models the number of failures in a sequence of independent and
     identically distributed Bernoulli trials before a specified (non-random)
     number of successes occurs.  It is defined by the number of successes R and
     the probability of success P.

     There are several ways to create a ‘NegativeBinomialDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor NegativeBinomialDistribution (R, P) to create a
          negative binomial distribution with fixed parameter values R and P.
        • Use the static method NegativeBinomialDistribution.fit (X, FREQ,
          OPTIONS) to fit a distribution to the data in X using the same input
          arguments as the ‘nbinfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the negative binomial distribution can be found
     at <https://en.wikipedia.org/wiki/Negative_binomial_distribution>

     See also: fitdist, makedist, nbincdf, nbininv, nbinpdf, nbinrnd, nbinfit,
     nbinlike, nbinstat.


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Negative binomial probability distribution object.



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NormalDistribution


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 -- statistics: NormalDistribution

     Normal probability distribution object.

     A ‘NormalDistribution’ object consists of parameters, a model description,
     and sample data for a normal probability distribution.

     The normal distribution is a continuous probability distribution that is
     symmetric about the mean, MU, showing that data near the mean are more
     frequent in occurrence than data far from the mean.  It is defined by
     location parameter MU and scale parameter SIGMA.

     There are several ways to create a ‘NormalDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor NormalDistribution (MU, SIGMA) to create a normal
          distribution with fixed parameter values MU and SIGMA.
        • Use the static method NormalDistribution.fit (X, CENSOR, FREQ,
          OPTIONS) to fit a distribution to data X.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the normal distribution can be found at
     <https://en.wikipedia.org/wiki/Normal_distribution>

     See also: fitdist, makedist, normcdf, norminv, normpdf, normrnd, normfit,
     normlike, normstat.


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Normal probability distribution object.



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PiecewiseLinearDistribution


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 -- statistics: PiecewiseLinearDistribution

     Piecewise linear probability distribution object.

     A ‘PiecewiseLinearDistribution’ object consists of parameters, a model
     description, and sample data for a piecewise linear probability
     distribution.

     The piecewise linear distribution is a continuous probability distribution
     that is defined by a set of points where the cumulative distribution
     function (CDF) changes slope.  It is defined by a vector of x values and a
     corresponding vector of CDF values FX.

     There are several ways to create a ‘PiecewiseLinearDistribution’ object.

        • Create a distribution with specified parameter values using the
          ‘makedist’ function.
        • Use the constructor PiecewiseLinearDistribution (X, FX) to create a
          piecewise linear distribution with specified parameter values X and
          FX.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the piecewise linear distribution can be found at
     <https://en.wikipedia.org/wiki/Piecewise_linear_function>

     See also: makedist, plcdf, plinv, plpdf, plrnd, plstat.


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Piecewise linear probability distribution object.



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PoissonDistribution


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 -- statistics: PoissonDistribution

     Poisson probability distribution object.

     A ‘PoissonDistribution’ object consists of parameters, a model description,
     and sample data for a Poisson probability distribution.

     The Poisson distribution is a discrete probability distribution that models
     the number of events occurring in a fixed interval of time or space, given
     a constant average rate of occurrence.  It is defined by the rate parameter
     LAMBDA.

     There are several ways to create a ‘PoissonDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor PoissonDistribution (LAMBDA) to create a Poisson
          distribution with fixed parameter value LAMBDA.
        • Use the static method PoissonDistribution.fit (X, FREQ) to fit a
          distribution to the data in X using the same input arguments as the
          ‘poissfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Poisson distribution can be found at
     <https://en.wikipedia.org/wiki/Poisson_distribution>

     See also: fitdist, makedist, poisscdf, poissinv, poisspdf, poissrnd,
     poissfit, poisslike, poisstat.


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Poisson probability distribution object.



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RayleighDistribution


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 -- statistics: RayleighDistribution

     Rayleigh probability distribution object.

     A ‘RayleighDistribution’ object consists of parameters, a model
     description, and sample data for a Rayleigh probability distribution.

     The Rayleigh distribution is a continuous probability distribution for
     nonnegative random variables.  It is often used to model the magnitude of a
     vector in two dimensions where the components are normally distributed with
     zero mean and equal variance.  It is defined by scale parameter SIGMA.

     There are several ways to create a ‘RayleighDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor RayleighDistribution (SIGMA) to create a Rayleigh
          distribution with fixed parameter value SIGMA.
        • Use the static method RayleighDistribution.fit (X, CENSOR, FREQ) to
          fit a distribution to the data in X using the same input arguments as
          the ‘raylfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Rayleigh distribution can be found at
     <https://en.wikipedia.org/wiki/Rayleigh_distribution>

     See also: fitdist, makedist, raylcdf, raylinv, raylpdf, raylrnd, raylfit,
     rayllike, raylstat.


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Rayleigh probability distribution object.



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RicianDistribution


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 -- statistics: RicianDistribution

     Rician probability distribution object.

     A ‘RicianDistribution’ object consists of parameters, a model description,
     and sample data for a Rician probability distribution.

     The Rician distribution is a continuous probability distribution that
     models the magnitude of a signal in the presence of Gaussian noise.  It is
     defined by noncentrality parameter S and scale parameter SIGMA.

     There are several ways to create a ‘RicianDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor RicianDistribution (S, SIGMA) to create a Rician
          distribution with fixed parameter values S and SIGMA.
        • Use the static method RicianDistribution.fit (X, CENSOR, FREQ,
          OPTIONS) to fit a distribution to data X.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Rician distribution can be found at
     <https://en.wikipedia.org/wiki/Rice_distribution>

     See also: fitdist, makedist, ricecdf, riceinv, ricepdf, ricernd, ricefit,
     ricelike, ricestat.


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Rician probability distribution object.



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TriangularDistribution


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 -- statistics: TriangularDistribution

     Triangular probability distribution object.

     A ‘TriangularDistribution’ object consists of parameters, a model
     description, and sample data for a triangular probability distribution.

     The triangular distribution uses the following parameters.

     PARAMETER            DESCRIPTION                            SUPPORT
                                                                 
     -----------------------------------------------------------------------------------
     A                    Lower limit                            -Inf < A < Inf
     B                    Peak location                          A <= B <= C
     C                    Upper limit                            C > A

     There are several ways to create a ‘TriangularDistribution’ object.

        • Create a distribution with specified parameter values using the
          ‘makedist’ function.
        • Use the constructor TriangularDistribution (A, B, C) to create a
          triangular distribution with specified parameter values A, B, and C.

     It is highly recommended to use ‘makedist’ function to create probability
     distribution objects, instead of the constructor.

     Further information about the triangular distribution can be found at
     <https://en.wikipedia.org/wiki/Triangular_distribution>

     See also: makedist, tricdf, triinv, tripdf, trirnd, tristat.


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Triangular probability distribution object.



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UniformDistribution


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 -- statistics: UniformDistribution

     Continuous uniform probability distribution object.

     A ‘UniformDistribution’ object consists of parameters, a model description,
     and sample data for a uniform probability distribution.

     The uniform distribution is a continuous probability distribution that
     models random variables that are equally likely to take any value within a
     specified interval defined by the lower limit LOWER and upper limit UPPER.

     There are several ways to create a ‘UniformDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor UniformDistribution (LOWER, UPPER) to create a
          uniform distribution with fixed parameter values LOWER and UPPER.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor.

     Further information about the continuous uniform distribution can be found
     at <https://en.wikipedia.org/wiki/Continuous_uniform_distribution>

     See also: fitdist, makedist, unifcdf, unifinv, unifpdf, unifrnd, unifit,
     unifstat.


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Continuous uniform probability distribution object.



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WeibullDistribution


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 -- statistics: WeibullDistribution

     Weibull probability distribution object.

     A ‘WeibullDistribution’ object consists of parameters, a model description,
     and sample data for a Weibull probability distribution.

     The Weibull distribution is a continuous probability distribution that
     models the time to failure of materials or the lifetime of mechanical
     systems.  It is defined by scale parameter LAMBDA and shape parameter K.

     There are several ways to create a ‘WeibullDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor WeibullDistribution (LAMBDA, K) to create a
          Weibull distribution with fixed parameter values LAMBDA and K.
        • Use the static method WeibullDistribution.fit (X, ALPHA, CENSOR, FREQ)
          to fit a distribution to the data in X using the same input arguments
          as the ‘wblfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the Weibull distribution can be found at
     <https://en.wikipedia.org/wiki/Weibull_distribution>

     See also: fitdist, makedist, wblcdf, wblinv, wblpdf, wblrnd, wblfit,
     wbllike, wblstat.


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Weibull probability distribution object.



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tLocationScaleDistribution


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 -- statistics: tLocationScaleDistribution

     Location-Scale Student's T probability distribution object.

     A ‘tLocationScaleDistribution’ object consists of parameters, a model
     description, and sample data for a location-scale Student's T probability
     distribution.

     The location-scale Student's T distribution is a continuous probability
     distribution that generalizes the standard Student's T distribution by
     including location and scale parameters.  It is defined by location
     parameter MU, scale parameter SIGMA, and degrees of freedom NU.

     There are several ways to create a ‘tLocationScaleDistribution’ object.

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with fixed parameter values using the ‘makedist’
          function.
        • Use the constructor tLocationScaleDistribution (MU, SIGMA, NU) to
          create a location-scale Student's T distribution with fixed parameter
          values MU, SIGMA, and NU.
        • Use the static method tLocationScaleDistribution.fit (X, CENSOR, FREQ,
          OPTIONS) to fit a distribution to the data in X using the same input
          arguments as the ‘tlsfit’ function.

     It is highly recommended to use ‘fitdist’ and ‘makedist’ functions to
     create probability distribution objects, instead of the class constructor
     or the aforementioned static method.

     Further information about the location-scale Student's T distribution can
     be found at
     <https://en.wikipedia.org/wiki/Student%27s_t-distribution#Location-scale_t_distribution>

     See also: fitdist, makedist, tlscdf, tlsinv, tlspdf, tlsrnd, tlsfit,
     tlslike, tlsstat.


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Location-Scale Student's T probability distribution object.





