Finding the convolution of two histograms?

In order to still move on (towards more murky details), I further adapted your code like this.

In order to still move on (towards more murky details), I further adapted your code like this: from numpy. Random import uniform from numpy import convolve, cumsum, histogram, linspace s, e, n= -0.5, 0.5, 1e3 x, y, bins= uniform(s, e, n), uniform(s, e, n), linspace(s, e, n** .75) pdf_x= histogram(x, normed= True, bins= bins)0 pdf_y= histogram(y, normed= True, bins= bins)0 c= convolve(pdf_x, pdf_y); c= c/ c.sum() bins= linspace(2* s, 2* e, len(c)) # a simulation xpy= uniform(s, e, 10* n)+ uniform(s, e, 10* n) c2= histogram(xpy, normed= True, bins= bins)0; c2= c2/ c2.sum() from pylab import grid, plot, show, subplot subplot(211), plot(bins, c) plot(linspace(xpy.min(), xpy.max(), len(c2)), c2, 'r'), grid(True) subplot(212), plot(bins, cumsum(c)), grid(True), show() Thus, giving plots something like this: Where the upper part represents the PDF (blue line), which indeed looks quite triangular and the simulation (red dots), which reflects the triangular shape. Lower part represents the CDF, which also looks to follow nicely the expected S-curve.

Thanks for the answer. Going according to the Wikipedia article on convolution, I would expect the new x range to run from -1 to 1. That's the part I'm struggling with.

– lafrasu Jun 29 at 20:23.

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