Hi Matthew, Thank you very much for the examples, I'll play around with them and see if I can cojole things into shape.
I've just been looking through the core/set.py and stats/*.py code (and brushing up with the Measure Theory chapters from Friedman's Foundations of Modern Analysis; it's been a while). I didn't twig to the * operator creating multi-dimensional intervals. Naming the axes would be nice but that's just a bookkeeping convenience. Unit measure isn't a biggie for me. Joint probabilities will almost certainly have to be estimated as if the two variables were independent. For my measurements, which are generated from a statistical fit, the model should (ideally, if not actually in theory) give me at least uncorrelated axes and I have an empirical distribution for the random variables that should at least let me scale everything to marginal values. As a first approximation it's probably good enough. On Monday, 12 November 2012 12:53:43 UTC-5, Matthew wrote: > > I'm not strictly convinced that that will work for all complex cases. You > should double-check your first results. Will do. I'll post my final notes and code. Thanks again -- Simon -- You received this message because you are subscribed to the Google Groups "sympy" group. To view this discussion on the web visit https://groups.google.com/d/msg/sympy/-/reAJWsiB2HkJ. To post to this group, send email to [email protected]. To unsubscribe from this group, send email to [email protected]. For more options, visit this group at http://groups.google.com/group/sympy?hl=en.
