MATLAB: Using MATLAB, what is the best way to find the integral of a bounded range of a CDF

cdfintegrationMATLAB

Using MATLAB, what is the best way to find the integral of a bounded range of a CDF. I've heard of trapz, cumtrapz, etc. but I do not know the best way to proceed for integrating CDFs. Please refer to the following code:

u = 1;
s = 1;
X = random('Normal',u,s,1,10000);
pd = makedist('Normal','mu',u,'sigma',s);
xAxis = min(X):.0001:max(X);
c_pd = cdf(pd,xAxis);
r = icdf(pd,[.3,.6]);
plot(xAxis,c_pd)

Basically, I am trying to integrate c_pd between the corresponding X values for .3 and .6 (found by using icdf). However, c_pd is a vector and not the actual cdf function. Does anyone have ideas on the best way to find the integral of this regardless of the distribution type (i.e. Normal, Rician, etc.)? Please advise. Thank you.

Best Answer

Well, you have the function as well, not just the vector. If I understand you correctly,

integral(@(x)cdf(pd,x),r(1),r(2))

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MATLAB: How to go about finding the standard normal probability based on the z-score

doc normcdf
doc normpdf

When you know what you want but not sure the name, try something like

>> lookfor normal
realmin                        - Smallest positive normalized floating point number.
randn                          - Normally distributed pseudorandom numbers.
sprandn                        - Sparse normally distributed random matrix.
surfnorm                       - Surface normals.
isonormals                     - Isosurface normals.
cde                            - cd elliptic function with normalized complex argument.
sne                            - sn elliptic function with normalized complex argument.
addfreqcsmenu                  - Add a cs menu to switch between linear and normalized frequency
convertfrequnits               - converts between Normalized, Hz, kHz, etc
histfit                        - Histogram with superimposed fitted normal density.
jbtest                         - Jarque-Bera hypothesis test of composite normality.
lhsnorm                        - Generate a latin hypercube sample with a normal distribution
logncdf                        - Lognormal cumulative distribution function (cdf).
lognfit                        - Parameter estimates and confidence intervals for lognormal data.
logninv                        - Inverse of the lognormal cumulative distribution function (cdf).
lognlike                       - Negative log-likelihood for the lognormal distribution.
lognpdf                        - Lognormal probability density function (pdf).
lognrnd                        - Random arrays from the lognormal distribution.
lognstat                       - Mean and variance for the lognormal distribution.
mvncdf                         - Multivariate normal cumulative distribution function (cdf).
mvnpdf                         - Multivariate normal probability density function (pdf).
mvnrnd                         - Random vectors from the multivariate normal distribution.
normcdf                        - Normal cumulative distribution function (cdf).
normfit                        - Parameter estimates and confidence intervals for normal data.
norminv                        - Inverse of the normal cumulative distribution function (cdf).
normlike                       - Negative log-likelihood for the normal distribution.
normpdf                        - Normal probability density function (pdf).
normplot                       - Displays a normal probability plot.
normrnd                        - Random arrays from the normal distribution.
normspec                       - Plots normal density between specification limits.
normstat                       - Mean and variance for the normal distribution.
logn3fit                       - Fit a 3-param lognormal dist'n using cumulative probabilities.
wgtnormfit                     - Fitting example for a weighted normal distribution.
wgtnormfit2                    - Fitting example for a weighted normal distribution (log(sigma) parameterization).
>>

Judicious search terms help but seeing the list of things related to "normal" lets you find the two functions of interest (plus a lot more depending upon which toolboxes are available, maybe) that might be of use/interest...

MATLAB: Question about PDF(probability density function)

Those terms are often used interchangeably. However, there is a clear distinction between

Cumulative distribution function, or cumulative probability distribution function

and the probability density function.

The cumulative distribution function is the P(X \leq x), the probability that a random variable X assumes a value less than or equal to x. For continuous random variables, the probability density function is the derivative of the cumulative distribution function.

Sometimes people use probability distribution function for discrete random variables where you can actually talk about the probability for an elementary outcome (unlike continuous random variables where you have to talk about probability on an interval). The term probability density function is NOT used for discrete random variables. For discrete random variables, people often use the term probability mass function.

Myself when I use the term distribution function, I mean the cumulative distribution function (for both continuous and discrete RVs). So you have to decide from context how it is being used.

Best Answer

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MATLAB: How to go about finding the standard normal probability based on the z-score

MATLAB: Question about PDF(probability density function)

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