Gamma Distribution

Fit, evaluate, and generate random samples from gamma distribution

Statistics and Machine Learning Toolbox™ offers several ways to work with the gamma distribution.

Create a probability distribution object GammaDistribution by fitting a probability distribution to sample data or by specifying parameter values. Then, use object functions to evaluate the distribution, generate random numbers, and so on.
Work with the gamma distribution interactively by using the Distribution Fitter app. You can export an object from the app and use the object functions.
Use distribution-specific functions with specified distribution parameters. The distribution-specific functions can accept parameters of multiple gamma distributions.
Use generic distribution functions (cdf, icdf, pdf, random) with a specified distribution name ('Gamma') and parameters.

To learn about the gamma distribution, see Gamma Distribution.

Objects

GammaDistribution Gamma probability distribution object

Distribution Fitter	Fit probability distributions to data
Probability Distribution Function	Interactive density and distribution plots

`makedist`	Create probability distribution object
`fitdist`	Fit probability distribution object to data

`cdf`	Cumulative distribution function
`gather`	Gather properties of Statistics and Machine Learning Toolbox object from GPU
`icdf`	Inverse cumulative distribution function
`iqr`	Interquartile range of probability distribution
`mean`	Mean of probability distribution
`median`	Median of probability distribution
`negloglik`	Negative loglikelihood of probability distribution
`paramci`	Confidence intervals for probability distribution parameters
`pdf`	Probability density function
`proflik`	Profile likelihood function for probability distribution
`random`	Random numbers
`std`	Standard deviation of probability distribution
`truncate`	Truncate probability distribution object
`var`	Variance of probability distribution

`gamcdf`	Gamma cumulative distribution function
`gampdf`	Gamma probability density function
`gaminv`	Gamma inverse cumulative distribution function
`gamlike`	Gamma negative log-likelihood
`gamstat`	Gamma mean and variance
`gamfit`	Gamma parameter estimates
`gamrnd`	Gamma random numbers
`randg`	Gamma random numbers with unit scale

`mle`	Maximum likelihood estimates
`mlecov`	Asymptotic covariance of maximum likelihood estimators

`distributionFitter`	Open Distribution Fitter app
`histfit`	Histogram with a distribution fit
`qqplot`	Quantile-quantile plot
`randtool`	Interactive random number generation

Gamma Distribution
The gamma distribution models sums of exponentially distributed random variables.