3 ms·

Yep — we assume that the input variables are independent. This is more acceptable in some settings than others, so as always, it's good to understand the limita

• sheikheddy 7y ago
  There has been some exploration into variance based global sensitivity analysis for models with dependent inputs. [1][2][3] [1]https://www.sciencedirect.com/science/article/pii/S0951832016301065 https://www.sciencedirect.com/science/article/pii/S095183201... [2]https://www.sciencedirect.com/science/article/pii/S0010465511004085 https://www.sciencedirect.com/science/article/pii/S001046551... [3]https://www.sciencedirect.com/science/article/pii/S0951832011001724 https://www.sciencedirect.com/science/article/pii/S095183201... I am not aware of any software package that implements these algorithms, although I am working towards creating or contributing to a python library that does. Ideally I'd publish a paper in a few months showing how the results (e.g, ranking of importance) differs from the independent case on particular datasets. Edit: It turns out SAlib recently added some of this functionality. I'll check it out!
• 6gvONxR4sf7o 7y ago
  I'm late to the party. Hoping you can answer a question. On the one hand, you're lowering the bar to do useful statistical work, which is awesome. A new set of people will be able to do better analysis. On the other hand, you're baking in assumptions which this new set of people won't understand, so you aren't necessarily lowering the bar to do correct statistical work. That seems challenging, and I'm curious how you think about it.