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 ofStatistics and Machine Learning Toolboxobject 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 theksdensityfunction.
合适的分布to Grouped Data Using ksdensity
This example shows how to fit kernel distributions to grouped sample data using theksdensityfunction.