Kernel Distribution

Fit a smoothed distribution based on a kernel function and evaluate the distribution

Functions

`fitdist`	Fit probability distribution object to data
`distributionFitter`	Open Distribution Fitter app
`ksdensity`	Kernel smoothing function estimate for univariate and bivariate data
`mvksdensity`	Kernel smoothing function estimate for multivariate 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
`pdf`	Probability density function
`random`	Random numbers
`std`	Standard deviation of probability distribution
`truncate`	Truncate probability distribution object
`var`	Variance of probability distribution

KernelDistribution Kernel probability distribution object

Kernel Distribution
A kernel distribution is a nonparametric representation of the probability density function of a random variable.
Nonparametric and Empirical Probability Distributions
Estimate a probability density function or a cumulative distribution function from sample data.
Fit Kernel Distribution Object to Data
This example shows how to fit a kernel probability distribution object to sample data.
Fit Kernel Distribution Using ksdensity
This example shows how to generate a kernel probability density estimate from sample data using the ksdensity function.
Fit Distributions to Grouped Data Using ksdensity
This example shows how to fit kernel distributions to grouped sample data using the ksdensity function.