ExponentialDistribution
              "upper")Return the CDF of a univariate distribution evaluated at x.
 cdf is a wrapper for the univariate cumulative distribution functions
 available in the statistics package.  See the corresponding functions’ help
 to learn the signification of the parameters after x.
 p = cdf (name, x, A) returns the CDF for the
 one-parameter distribution family specified by name and the
 distribution parameter A, evaluated at the values in x.
 p = cdf (name, x, A, B) returns the CDF
 for the two-parameter distribution family specified by name and the
 distribution parameters A and B, evaluated at the values in
 x.
 p = cdf (name, x, A, B, C) returns
 the CDF for the three-parameter distribution family specified by name
 and the distribution parameters A, B, and C, evaluated at
 the values in x.
 p = cdf (…,  returns the complement of the
 CDF using an algorithm that more accurately computes the extreme upper-tail
 probabilities.  "upper")"upper" can follow any of the input arguments in the
 previous syntaxes.
 name must be a char string of the name or the abbreviation of the
 desired cumulative distribution function as listed in the followng table.
 The last column shows the number of required parameters that should be parsed
 after x to the desired CDF.  The optional input argument
 "upper" does not count in the required number of parameters.
| Distribution Name | Abbreviation | Input Parameters | ||
|---|---|---|---|---|
| "Beta" | "beta" | 2 | ||
| "Binomial" | "bino" | 2 | ||
| "Birnbaum-Saunders" | "bisa" | 2 | ||
| "Burr" | "burr" | 3 | ||
| "Cauchy" | "cauchy" | 2 | ||
| "Chi-squared" | "chi2" | 1 | ||
| "Extreme Value" | "ev" | 2 | ||
| "Exponential" | "exp" | 1 | ||
| "F-Distribution" | "f" | 2 | ||
| "Gamma" | "gam" | 2 | ||
| "Geometric" | "geo" | 1 | ||
| "Generalized Extreme Value" | "gev" | 3 | ||
| "Generalized Pareto" | "gp" | 3 | ||
| "Gumbel" | "gumbel" | 2 | ||
| "Half-normal" | "hn" | 2 | ||
| "Hypergeometric" | "hyge" | 3 | ||
| "Inverse Gaussian" | "invg" | 2 | ||
| "Laplace" | "laplace" | 2 | ||
| "Logistic" | "logi" | 2 | ||
| "Log-Logistic" | "logl" | 2 | ||
| "Lognormal" | "logn" | 2 | ||
| "Nakagami" | "naka" | 2 | ||
| "Negative Binomial" | "nbin" | 2 | ||
| "Noncentral F-Distribution" | "ncf" | 3 | ||
| "Noncentral Student T" | "nct" | 2 | ||
| "Noncentral Chi-Squared" | "ncx2" | 2 | ||
| "Normal" | "norm" | 2 | ||
| "Poisson" | "poiss" | 1 | ||
| "Rayleigh" | "rayl" | 1 | ||
| "Rician" | "rice" | 2 | ||
| "Student T" | "t" | 1 | ||
| "location-scale T" | "tls" | 3 | ||
| "Triangular" | "tri" | 3 | ||
| "Discrete Uniform" | "unid" | 1 | ||
| "Uniform" | "unif" | 2 | ||
| "Von Mises" | "vm" | 2 | ||
| "Weibull" | "wbl" | 2 | 
See also: icdf, pdf, cdf, betacdf, binocdf, bisacdf, burrcdf, cauchycdf, chi2cdf, evcdf, expcdf, fcdf, gamcdf, geocdf, gevcdf, gpcdf, gumbelcdf, hncdf, hygecdf, invgcdf, laplacecdf, logicdf, loglcdf, logncdf, nakacdf, nbincdf, ncfcdf, nctcdf, ncx2cdf, normcdf, poisscdf, raylcdf, ricecdf, tcdf, tlscdf, tricdf, unidcdf, unifcdf, vmcdf, wblcdf
Source Code: ExponentialDistribution
icdf
                  Compute the cumulative distribution function (CDF).
 p = icdf (pd, x) computes the quantile (the
 inverse of the CDF) of the probability distribution object, pd,
 evaluated at the values in x.
iqr
                  Compute the interquartile range of a probability distribution.
 r = iqr (pd) computes the interquartile range of the
 probability distribution object, pd.
mean
                  Compute the mean of a probability distribution.
 m = mean (pd) computes the mean of the probability
 distribution object, pd.
median
                  Compute the median of a probability distribution.
 m = median (pd) computes the median of the probability
 distribution object, pd.
negloglik
                  Compute the negative loglikelihood of a probability distribution.
 m = negloglik (pd) computes the negative loglikelihood
 of the probability distribution object, pd.
paramci
                  Compute the confidence intervals for probability distribution parameters.
 ci = paramci (pd) computes the lower and upper
 boundaries of the 95% confidence interval for each parameter of the
 probability distribution object, pd.
 ci = paramci (pd, Name, Value) computes the
 confidence intervals with additional options specified specified by
 Name-Value pair arguments listed below.
| Name | Value | |
|---|---|---|
| "Alpha" | A scalar value in the range specifying the significance level for the confidence interval. The default value 0.05 corresponds to a 95% confidence interval. | |
| "Parameter" | A character vector or a cell array of
 character vectors specifying the parameter names for which to compute
 confidence intervals.  By default, paramcicomputes confidence
 intervals for all distribution parameters. | 
 paramci is meaningful only when pd is fitted to data,
 otherwise an empty array, [], is returned.
pdf
                  Compute the probability distribution function (PDF).
 y = pdf (pd, x) computes the PDF of the
 probability distribution object, pd, evaluated at the values in
 x.
plot
                  Plot a probability distribution object.
 plot (pd plots a probability density function (PDF) of the
 probability distribution object pd.  If pd contains data,
 which have been fitted by fitdist, the PDF is superimposed over a
 histogram of the data.
 plot (pd, Name, Value) specifies additional
 options with the Name-Value pair arguments listed below.
| Name | Value | |
|---|---|---|
| "PlotType" | A character vector specifying the plot
 type. "pdf"plots the probability density function (PDF).  When
 pd is fit to data, the PDF is superimposed on a histogram of the
 data."cdf"plots the cumulative density function (CDF).  When
 pd is fit to data, the CDF is superimposed over an empirical CDF."probability"plots a probability plot using a CDF of the data
 and a CDF of the fitted probability distribution.  This option is
 available only when pd is fitted to data. | |
| "Discrete" | A logical scalar to specify whether to
 plot the PDF or CDF of a discrete distribution object as a line plot or a
 stem plot, by specifying falseortrue, respectively.  By
 default, it istruefor discrete distributions andfalsefor continuous distributions.  When pd is a continuous distribution
 object, option is ignored. | |
| "Parent" | An axes graphics object for plot.  If
 not specified, the plotfunction plots into the current axes or
 creates a new axes object if one does not exist. | 
 h = plot (…) returns a graphics handle to the plotted
 objects.
proflik
                  "Display", display)"Display", display)Profile likelihood function for a probability distribution object.
 [nlogL, param] = proflik (pd, pnum)
 returns a vector nlogL of negative loglikelihood values and a
 vector param of corresponding parameter values for the parameter in
 the position indicated by pnum.  By default, proflik uses
 the lower and upper bounds of the 95% confidence interval and computes
 100 equispaced values for the selected parameter.  pd must be
 fitted to data.
 [nlogL, param] = proflik (pd, pnum,
  also plots the profile likelihood
 against the default range of the selected parameter.
"Display", "on")
 [nlogL, param] = proflik (pd, pnum,
 setparam) defines a user-defined range of the selected parameter.
 [nlogL, param] = proflik (pd, pnum,
 setparam,  also plots the profile
 likelihood against the user-defined range of the selected parameter.
"Display", "on")
 For the exponential distribution, pnum = 1 selects the
 parameter mu.
 When opted to display the profile likelihood plot, proflik also
 plots the baseline loglikelihood computed at the lower bound of the 95%
 confidence interval and estimated maximum likelihood.  The latter might
 not be observable if it is outside of the used-defined range of parameter
 values.
random
                  Generate random arrays from the probability distribution object.
 r = random (pd) returns a random number from the
 distribution object pd.
 When called with a single size argument, betarnd returns a square
 matrix with the dimension specified.  When called with more than one
 scalar argument, the first two arguments are taken as the number of rows
 and columns and any further arguments specify additional matrix
 dimensions.  The size may also be specified with a row vector of
 dimensions, sz.
std
                  Compute the standard deviation of a probability distribution.
 s = std (pd) computes the standard deviation of the
 probability distribution object, pd.
truncate
                  Truncate a probability distribution.
 t = truncate (pd) returns a probability distribution
 t, which is the probability distribution pd truncated to the
 specified interval with lower limit, lower, and upper limit,
 upper.  If pd is fitted to data with fitdist, the
 returned probability distribution t is not fitted, does not contain
 any data or estimated values, and it is as it has been created with the
 makedist function, but it includes the truncation interval.
var
                  Compute the variance of a probability distribution.
 v = var (pd) computes the standard deviation of the
 probability distribution object, pd.