t Location-Scale Distribution

Fit, evaluate, and generate random samples from t location-scale distribution

Statistics and Machine Learning Toolbox™ offers multiple ways to work with the t location-scale distribution.

Create a tLocationScaleDistribution object and use tLocationScaleDistribution object functions.
Use the generic distribution functions with the specified distribution name "tLocationScale" and corresponding parameters.

To learn about the t location-scale distribution, see t Location-Scale Distribution.

Functions

`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 (Since R2020b)
`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
`plot`	Plot probability distribution object (Since R2022b)
`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

`cdf`	Cumulative distribution function
`icdf`	Inverse cumulative distribution function
`pdf`	Probability density function
`random`	Random numbers
`mle`	Maximum likelihood estimates

tLocationScaleDistribution t Location-Scale probability distribution object

Fit probability distributions to data

t Location-Scale Distribution
The t location-scale distribution is useful for modeling data distributions with heavier tails (more prone to outliers) than the normal distribution.
Represent Cauchy Distribution Using t Location-Scale
This example shows how to use the t location-scale probability distribution object to work with a Cauchy distribution with nonstandard parameter values.