How to fit multivariate pdf and cdf from data
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I have a set of simulated data from a Monte Carlo simulation which gives me a bivariate distribution. I can plot the results using histogram2, and I expect the results to be bivariate gaussian. How can I properly fit this 'empirical' data to get a normalized pdf and cdf which I can then integrate over to get some confidence intervals?
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Accepted Answer
Jeff Miller
on 1 Jul 2018
You don't need a bivariate histogram to fit the bivariate normal--just use the sample means and covariance matrix. Here's an example:
% Let's say your data are in an n,2 matrix called xy.
% Here is one randomly generated to use in the example.
muXY = [100, 200];
sigmaXY = [15^2, 5^2; 5^2, 20^2];
xy = mvnrnd(muXY,sigmaXY,10000);
% Here is your bivariate histogram:
figure; histogram2(xy(:,1),xy(:,2));
% Now estimate the parameters of the best-fitting Gaussian:
xybar = mean(xy);
xycovar=cov(xy);
% Plot the best-fitting bivariate pdf:
xsteps = min(xy(:,1)):1:max(xy(:,1)); % Adjust with step sizes appropriate for your
ysteps = min(xy(:,2)):1:max(xy(:,2)); % x and y values.
[X,Y] = meshgrid(xsteps,ysteps);
F = mvnpdf([X(:) Y(:)],xybar,xycovar); % Note that xybar and xycovar are used here.
F = reshape(F,length(ysteps),length(xsteps));
figure; surf(xsteps,ysteps,F);
caxis([min(F(:))-.5*range(F(:)),max(F(:))]);
xlabel('x'); ylabel('y'); zlabel('Probability Density');
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More Answers (1)
dpb
on 30 Jun 2018
Edited: dpb
on 30 Jun 2018
They're in the BinCounts property of the object or you can just use the old histcounts2.
ADDENDUM
Ah, ok. I've not tried in Matlab, seems a definite lack of no prepared function indeed...
Attach your data and I'll try to see if I can give it a go later on...btw, you'll probably get much better fit using the raw data than histogram bin counts.
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