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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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     a                  1st Shape parameter                 α > 0
     b                  2nd Shape parameter                 β > 0

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor BetaDistribution (A, B) to create a beta
          distribution with specified parameter values.
        • Use the static method BetaDistribution.fit (X, CENSOR, FREQ,
          OPTIONS) to a distribution to data X.

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

     A ‘BetaDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘BetaDistribution’ object contains the following methods: ‘cdf’,
     ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’, ‘pdf’,
     ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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,
     lognfit, 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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     N                  Number of trials                    positive integer
     p                  Probability of success              0 <= p <= 1

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor BinomialDistribution (N, P) to create a
          binomial distribution with specified parameter values.
        • Use the static method BinomialDistribution.fit (X, CENSOR,
          FREQ, OPTIONS) to a distribution to data X.

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

     A ‘BinomialDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘BinomialDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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

     Gamma probability distribution object.

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

     The gamma distribution uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     a                  Shape parameter                     α > 0
     b                  Scale parameter                     β > 0

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor BirnbaumSaundersDistribution (A, B) to
          create a gamma distribution with specified parameter values.
        • Use the static method BirnbaumSaundersDistribution.fit (X,
          CENSOR, FREQ, OPTIONS) to a distribution to data X.

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

     A ‘BirnbaumSaundersDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘BirnbaumSaundersDistribution’ object contains the following
     methods: ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’,
     ‘paramci’, ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’,
     ‘var’.

     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,
     lognfit, bisalike, bisastat.


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Gamma 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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     alpha              Shape                               -Inf < alpha <
                                                            Inf
     c                  Scale                               c > 0
     k                  Location                            -Inf < k < Inf

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor BurrDistribution (ALPHA, C) to create a
          generalized extreme value distribution with specified
          parameter values.
        • Use the static method BurrDistribution.fit (X, ALPHA, FREQ) to
          a distribution to data X.

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

     A ‘BurrDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘BurrDistribution’ object contains the following methods: ‘cdf’,
     ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’, ‘pdf’,
     ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Mean                                mu > 0

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

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

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

     A ‘ExponentialDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘ExponentialDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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 a extreme value probability
     distribution.

     The extreme value distribution uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Location parameter                  -Inf < mu < Inf
     sigma              Scale parameter                     sigma > 0

     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 a extreme value distribution with specified parameter
          values.
        • Use the static method ExtremeValueDistribution.fit (X, CENSOR,
          FREQ, OPTIONS) to a distribution to data X.

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

     A ‘ExtremeValueDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘ExtremeValueDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     a                  Shape parameter                     α > 0
     b                  Scale parameter                     β > 0

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor GammaDistribution (A, B) to create a gamma
          distribution with specified parameter values.
        • Use the static method GammaDistribution.fit (X, CENSOR, FREQ,
          OPTIONS) to a distribution to data X.

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

     A ‘GammaDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘GammaDistribution’ object contains the following methods: ‘cdf’,
     ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’, ‘pdf’,
     ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

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

     See also: fitdist, makedist, gamcdf, gaminv, gampdf, gamrnd,
     lognfit, 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 uses the following
     parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     k                  Shape                               -Inf < k < Inf
     sigma              Scale                               sigma > 0
     mu                 Location                            -Inf < mu < Inf

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor GeneralizedExtremeValueDistribution (K,
          SIGMA) to create a generalized extreme value distribution with
          specified parameter values.
        • Use the static method GeneralizedExtremeValueDistribution.fit
          (X, K, FREQ) to a distribution to data X.

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

     A ‘GeneralizedExtremeValueDistribution’ object contains the
     following properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘GeneralizedExtremeValueDistribution’ object contains the
     following methods: ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’,
     ‘negloglik’, ‘paramci’, ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’,
     ‘truncate’, ‘var’.

     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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     k                  Shape                               -Inf < k < Inf
     sigma              Scale                               sigma > 0
     theta              Location                            -Inf < theta <
                                                            Inf

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor GeneralizedParetoDistribution (K, SIGMA)
          to create a generalized Pareto distribution with specified
          parameter values.
        • Use the static method GeneralizedParetoDistribution.fit (X, K,
          FREQ) to a distribution to data X.

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

     A ‘GeneralizedParetoDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘GeneralizedParetoDistribution’ object contains the following
     methods: ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’,
     ‘paramci’, ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’,
     ‘var’.

     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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Location                            -Inf < mu < Inf
     sigma              Scale                               sigma > 0

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor HalfNormalDistribution (MU, SIGMA) to
          create a half-normal distribution with specified parameter
          values.
        • Use the static method HalfNormalDistribution.fit (X, MU, FREQ)
          to a distribution to data X.

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

     A ‘HalfNormalDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘HalfNormalDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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

     Logistic probability distribution object.

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

     The logistic distribution uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Scale parameter                     mu >= 0
     lambda             Shape parameter                     lambda > 0

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor InverseGaussianDistribution (MU, LAMBDA)
          to create a logistic distribution with specified parameter
          values.
        • Use the static method InverseGaussianDistribution.fit (X,
          CENSOR, FREQ, OPTIONS) to a distribution to data X.

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

     A ‘InverseGaussianDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘InverseGaussianDistribution’ object contains the following
     methods: ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’,
     ‘paramci’, ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’,
     ‘var’.

     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,
     lognfit, invglike, invgstat.


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Logistic 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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Mean of logarithmic values          mu >= 0
     sigma              Scale of logarithmic values         sigma > 0

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

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

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

     A ‘LogisticDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘LogisticDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

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

     OCTAVE/MATLAB use an alternative parameterization given by the pair
     μ, s, i.e.  MU and SIGMA, in analogy with the logistic
     distribution.  Their relation to the α and b parameters used in
     Wikipedia are given below:

        • MU = log (A)
        • SIGMA = 1 / A

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


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Logistic probability distribution object.



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LoglogisticDistribution


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 -- statistics: LoglogisticDistribution

     Loglogistic probability distribution object.

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

     The loglogistic distribution uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Mean of logarithmic values          mu >= 0
     sigma              Scale of logarithmic values         sigma > 0

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

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

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

     A ‘LoglogisticDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘LoglogisticDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

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

     OCTAVE/MATLAB use an alternative parameterization given by the pair
     μ, s, i.e.  MU and SIGMA, in analogy with the logistic
     distribution.  Their relation to the α and b parameters used in
     Wikipedia are given below:

        • MU = log (A)
        • SIGMA = 1 / A

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


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Loglogistic 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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Mean of logarithmic values          -Inf < mu < Inf
     sigma              Standard deviation of logarithmic   sigma > 0
                        values

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

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

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

     A ‘LognormalDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘LognormalDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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

     Loguniform probability distribution object.

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

     The loguniform distribution uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     Lower              Lower limit                         0 < Lower < Upper
     Upper              Upper limit                         Lower < Upper <
                                                            Inf

     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) to create a
          loguniform distribution with specified parameter values.

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

     A ‘LoguniformDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   Truncation         IsTruncated

     A ‘LoguniformDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘pdf’, ‘plot’, ‘random’,
     ‘std’, ‘truncate’, ‘var’.

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

     See also: fitdist, makedist.


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Loguniform 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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     Probabilities      Outcome probabilities               0 <=
                                                            Probabilities(i)
                                                            <= 1; sum_i
                                                            (Probabilities) =
                                                            1

     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.

     A ‘MultinomialDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   Truncation         IsTruncated

     A ‘MultinomialDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘pdf’, ‘plot’, ‘random’,
     ‘std’, ‘truncate’, ‘var’.

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

     See also: fitdist, makedist, mnpdf, mnrnd.


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Multinomial probability distribution object.



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NakagamiDistribution


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 -- statistics: NakagamiDistribution

     Normal probability distribution object.

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

     The normal distribution uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Number of successes                 mu > 0
     omega              Probability of success              omega > 0

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

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

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

     A ‘NakagamiDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘NakagamiDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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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Normal 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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     R                  Number of successes                 R > 0
     P                  Probability of success              0 < P <= 1

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor NegativeBinomialDistribution (R, P) to
          create a negative binomial distribution with specified
          parameter values.
        • Use the static method NegativeBinomialDistribution.fit (X,
          CENSOR, FREQ, OPTIONS) to a distribution to data X.

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

     A ‘NegativeBinomialDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘NegativeBinomialDistribution’ object contains the following
     methods: ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’,
     ‘paramci’, ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’,
     ‘var’.

     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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Mean                                -Inf < mu < Inf
     sigma              Standard deviation                  sigma > 0

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

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

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

     A ‘NormalDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘NormalDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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

     Continuous uniform probability distribution object.

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

     The uniform distribution uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     x                  Vector of x values at which the     -Inf < x < Fx
                        cdf changes slope
     Fx                 Vector of CDF values that           0 <= Fx <= 1
                        correspond to each value in x

     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 uniform distribution with specified parameter values.

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

     A ‘PiecewiseLinearDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   Truncation         IsTruncated

     A ‘PiecewiseLinearDistribution’ object contains the following
     methods: ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘pdf’, ‘plot’,
     ‘random’, ‘std’, ‘truncate’, ‘var’.

     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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Continuous uniform 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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     lambda             Rate parameter                      lambda > 0

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor PoissonDistribution (LAMBDA) to create a
          Poisson distribution with specified parameter values.
        • Use the static method PoissonDistribution.fit (X, CENSOR,
          FREQ, OPTIONS) to a distribution to data X.

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

     A ‘PoissonDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘PoissonDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     Further information about the Poisson distribution can be found at
     <https://en.wikipedia.org/wiki/Rayleigh_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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     sigma              Scale parameter                     sigma > 0

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

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

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

     A ‘RayleighDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘RayleighDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     s                  Noncentrality parameter             s >= 0
     sigma              Scale parameter                     sigma > 0

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

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

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

     A ‘RicianDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘RicianDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

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

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

     A ‘TriangularDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   Truncation         IsTruncated

     A ‘TriangularDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘pdf’, ‘plot’, ‘random’,
     ‘std’, ‘truncate’, ‘var’.

     Further information about the continuous triangular distribution
     can be found at
     <https://en.wikipedia.org/wiki/Continuous_uniform_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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     Lower              Lower limit                         -Inf < Lower <
                                                            Upper
     Upper              Upper limit                         Lower < Upper <
                                                            Inf

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

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

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

     A ‘UniformDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   Truncation         IsTruncated

     A ‘UniformDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘pdf’, ‘plot’, ‘random’,
     ‘std’, ‘truncate’, ‘var’.

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

     See also: 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 uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     lambda             Scale parameter                     lambda > 0
     k                  Scale parameter                     k > 0

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor WeibullDistribution (LAMBDA, K) to create
          a Weibull distribution with specified parameter values.
        • Use the static method WeibullDistribution.fit (X, CENSOR,
          FREQ, OPTIONS) to a distribution to data X.

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

     A ‘WeibullDistribution’ object contains the following properties,
     which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘WeibullDistribution’ object contains the following methods:
     ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’, ‘paramci’,
     ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’, ‘var’.

     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, wblplot.


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Weibull probability distribution object.



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tLocationScaleDistribution


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 -- statistics: tLocationScaleDistribution

     Weibull probability distribution object.

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

     The Weibull distribution uses the following parameters.

     PARAMETER          DESCRIPTION                         SUPPORT
                                                            
     ---------------------------------------------------------------------------
     mu                 Location parameter                  -Inf < mu < Inf
     sigma              Scale parameter                     sigma > 0
     nu                 Degrees of Freedom                  nu > 0

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

        • Fit a distribution to data using the ‘fitdist’ function.
        • Create a distribution with specified parameter values using
          the ‘makedist’ function.
        • Use the constructor tLocationScaleDistribution (LAMBDA, MU) to
          create a Weibull distribution with specified parameter values.
        • Use the static method tLocationScaleDistribution.fit (X,
          CENSOR, FREQ, OPTIONS) to a distribution to data X.

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

     A ‘tLocationScaleDistribution’ object contains the following
     properties, which can be accessed using dot notation.

     DistributionName   DistributionCode   NumParameters      ParameterNames
     ParameterDescriptionParameterValues   ParameterValues    ParameterCI
     ParameterIsFixed   Truncation         IsTruncated        InputData

     A ‘tLocationScaleDistribution’ object contains the following
     methods: ‘cdf’, ‘icdf’, ‘iqr’, ‘mean’, ‘median’, ‘negloglik’,
     ‘paramci’, ‘pdf’, ‘plot’, ‘proflik’, ‘random’, ‘std’, ‘truncate’,
     ‘var’.

     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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Weibull probability distribution object.





