ChrisRackauckas OP t1_ist3bu8 wrote
Reply to comment by PolygonAndPixel2 in [P] Stochastic Differentiable Programming: Unbiased Automatic Differentiation for Discrete Stochastic Programs (such as particle filters, agent-based models, and more!) by ChrisRackauckas
> What do you mean by that or rather what is a program that is not mathematical?
If it outputs strings or code it may not work with this method. It should output numbers in a way that has a well-defined (differentiable) expectation.
> Is it correct to say that the execution path of the program changes with different outcomes for p, i.e., if(random_event(p)) {return 1;} else {return 0;}? Or is this a different problem?
It can. One of the examples is differentiation of an inhomogeneous random walk, which is a stress test of doing this kind of branch handling.
> but can you estimate how much work it is to incorporate this kind of AD in existing AD tools like CoDiPack?
That's hard to say. I would say it wouldn't be "too hard", though it may be hard to add this without overhead for "normal" cases? It would make the code more complicated but the standard cases are just a special case here, so it should be fine.
PolygonAndPixel2 t1_ist8frv wrote
Thanks for your answer. That all makes sense and I'm going to take a closer look at the random walk.
Viewing a single comment thread. View all comments