# probgrid

Nonuniformly spaced probabilities

Since R2021a

## Description

example

p = probgrid(p1,p2) returns a nonuniformly spaced array of 100 probabilities between p1 and p2 that correspond to the values of the normal cumulative distribution function (CDF) evaluated over a set of points uniformly spaced in the domain of the normal distribution.

example

p = probgrid(p1,p2,n) returns an array of n probabilities.

## Examples

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Evaluate the standard normal cumulative distribution function (CDF) on a 10-point grid between 0.2 and 0.95. Determine the points that correspond to the probabilities by evaluating the inverse normal CDF, also known as the probit function.

pmin = 0.2;
pmax = 0.95;
N = 10;

pd = probgrid(pmin,pmax,N);

xd = sqrt(2)*erfinv(2*pd-1);

Plot the standard normal CDF and overlay the points generated by probgrid.

x = -3:0.01:3;
sncdf = (1+erf(x/sqrt(2)))/2;

plot(x,sncdf)

hold on
plot(xd,pd,'o')
hold off

legend({'Standard Normal CDF','Probability Vector'}, ...
'Location','Northwest')
xticks(xd)
xtickangle(40)
yticks(round(100*pd)/100)
ylabel('Probability')
grid on

## Input Arguments

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Interval endpoints, specified as scalars from the interval [0, 1]. p1 and p2 must obey p1 < p2.

Data Types: double

Number of samples in probability grid, specified as a positive integer scalar.

Data Types: double

## Output Arguments

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Array of probabilities, returned as a row vector.

## Version History

Introduced in R2021a