I have a collection of processes that will each end at some random real-valued point in time, and at a particular time I am going to examine all the data I have collected for these processes.
There is a probability density function that governs how long these processes each last, p(X|\mu), and I want to estimate \mu. The values that have ended correspond to points in the density function, but those that have not ended correspond to points in the CDF, ergo actual probabilities. I'm not sure how to reconcile these into a single likelihood function. Is anyone knowledgable?