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.
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.
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
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