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

Another suggestion is to use kernel densities. Especially if you use Gaussian kernels, then they can be relatively easy to work with... except that the distributions quickly explode in size without care. Depending on the application, there are additional approximation techniques like importance sampling that can be used.