A character vector specifying the name of the probability distribution object. This property is read-only.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

A character vector specifying the type of smoothing kernel used for the density estimate. It is one of 'normal', 'box', 'triangle', or 'epanechnikov'. You can access the Kernel property using dot name assignment.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

A positive scalar value specifying the bandwidth of the smoothing kernel. You can access the Bandwidth property using dot name assignment.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

A scalar structure containing the following fields:

• range: either the character vector 'unbounded' or 'positive', or a two-element numeric vector $[L,\; U]$ with the lower and upper bounds of the support.
• closedbound: a two-element logical vector specifying whether each bound is closed.
• iscontinuous: a logical scalar, always true for a kernel distribution.

This property is read-only.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

A $1\times 2$ numeric vector specifying the truncation interval for the probability distribution. First element contains the lower boundary, second element contains the upper boundary. This property is read-only. You can only truncate a probability distribution with the truncate method.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

A logical scalar value specifying whether a probability distribution is truncated or not. This property is read-only.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

A scalar structure containing the following fields:

• data: a numeric vector containing the data used for distribution fitting.
• cens: an empty array, since censoring is not supported for a kernel distribution.
• freq: a numeric vector of non-negative integer values containing the frequency information corresponding to the elements of the data used for distribution fitting. If no frequency vector was used for distribution fitting, then this field defaults to an empty array.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: p = cdf (pd, x)

prob.KernelDistribution: p = cdf (pd, x, 'upper')

p = cdf (pd, x) computes the CDF of the probability distribution object, pd, evaluated at the values in x.

p = cdf (…, 'upper') returns the complement of the CDF of the probability distribution object, pd, evaluated at the values in x.

x must be double or single; integer, logical, and character arrays are rejected.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: x = icdf (pd, p)

x = icdf (pd, p) computes the quantile (the inverse of the CDF) of the probability distribution object, pd, evaluated at the values in p.

p must be double or single; integer, logical, and character arrays are rejected.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: r = iqr (pd)

r = iqr (pd) computes the interquartile range of the probability distribution object, pd.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: m = mean (pd)

m = mean (pd) computes the mean of the probability distribution object, pd.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: m = median (pd)

m = median (pd) computes the median of the probability distribution object, pd.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: nlogL = negloglik (pd)

nlogL = negloglik (pd) computes the negative loglikelihood of the probability distribution object, pd.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: y = pdf (pd, x)

y = pdf (pd, x) computes the PDF of the probability distribution object, pd, evaluated at the values in x.

x must be double or single; integer, logical, and character arrays are rejected.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: plot (pd)

prob.KernelDistribution: plot (pd, Name, Value)

prob.KernelDistribution: h = plot (…)

plot (pd) plots a probability density function (PDF) of the probability distribution object pd, superimposed over a histogram of the data used to fit it.

plot (pd, Name, Value) specifies additional options with the Name-Value pair arguments listed below.

h = plot (…) returns a graphics handle to the plotted objects.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: r = random (pd)

prob.KernelDistribution: r = random (pd, rows)

prob.KernelDistribution: r = random (pd, rows, cols, …)

prob.KernelDistribution: r = random (pd, [sz])

r = random (pd) returns a random number from the distribution object pd.

When called with a single size argument, random returns a square matrix with the dimension specified. When called with more than one scalar argument, the first two arguments are taken as the number of rows and columns and any further arguments specify additional matrix dimensions. The size may also be specified with a row vector of dimensions, sz.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: s = std (pd)

s = std (pd) computes the standard deviation of the probability distribution object, pd.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: t = truncate (pd, lower, upper)

t = truncate (pd, lower, upper) returns a probability distribution t, which is the probability distribution pd truncated to the specified interval with lower limit, lower, and upper limit, upper.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

prob.KernelDistribution: v = var (pd)

v = var (pd) computes the variance of the probability distribution object, pd.

Example: 1

Fit a kernel distribution to a sample and plot its PDF over a histogram.

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')

 load patients
 pd = fitdist (Weight, 'Kernel');
 plot (pd)
 title ('Kernel distribution fitted to patient weights')