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Kernel Distribution

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

Functions

fitdist Fit probability distribution object to data
dfittool Open Distribution Fitting app
ksdensity Kernel smoothing function estimate for univariate and bivariate data
mvksdensity Kernel smoothing function estimate for multivariate data
cdf Cumulative distribution functions
icdf Inverse cumulative distribution functions
iqr Interquartile range
mean Mean of probability distribution
median Median of probability distribution
negloglik Negative log likelihood of probability distribution
pdf Probability density functions
random Random numbers
std Standard deviation of probability distribution
truncate Truncate probability distribution object
var Variance of probability distribution

Using Objects

KernelDistribution Kernel probability distribution object

Examples and How To

Fit Kernel Distribution Object to Data

Fit a kernel probability distribution object to sample data.

Fit Kernel Distribution Using ksdensity

Generate a kernel probability density estimate from sample data using the ksdensity function.

Fit Probability Distribution Objects to Grouped Data

Fit probability distribution objects to grouped sample data, and create a plot to visually compare the pdf of each group.

Fit Distributions to Grouped Data Using ksdensity

Fit kernel distributions to grouped sample data using the ksdensity function.

Compare Multiple Distribution Fits

Fit multiple probability distribution objects to the same set of sample data, and obtain a visual comparison of how well each distribution fits the data.

Concepts

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.

Grouping Variables

Grouping variables are utility variables used to group or categorize observations.

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