Representing continuous probability distributions

Question

I have a problem involving a collection of continuous probability distribution functions, most of which are determined empirically (e.g. departure times, transit times). What I

Answer 1

I worked on similar problems for my dissertation.

One way to compute approximate convolutions is to take the Fourier transform of the density functions (histograms in this case), multiply them, then take the inverse Fourier transform to get the convolution.

Look at Appendix C of my dissertation for formulas for various special cases of operations on probability distributions. You can find the dissertation at: http://riso.sourceforge.net

I wrote Java code to carry out those operations. You can find the code at: https://sourceforge.net/projects/riso