> 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. >
I agree that this is inconvenient. SymPy stats should really be doing this bookkeeping for you. > 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. > If you do go digging around in the code I'd probably suggest working with sympy.core.sets rather than sympy.stats. sympy.core.sets is better organized and has a much lower entry barrier. -- You received this message because you are subscribed to the Google Groups "sympy" group. To post to this group, send email to sympy@googlegroups.com. To unsubscribe from this group, send email to sympy+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/sympy?hl=en.
