You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Sometimes, SBI gets stuck because of some numerical instability.
Solution
Implements a logit transformation on the conditioning variable bounded within a given interval (min_val, max_val), mapping it to the real line (-inf, inf). This transformation is useful for normalizing bounded data while ensuring numerical stability.
Instead of z_scoring the conditioning variable, one can now also logit-transform it. Allowing in some cases, a more stable evaluation with the rejection-sampling algorithm (if normalized posterior is required).
📌 Additional Context
I have had these issues before with SBI, and implementing a logit transformation helped resolve the numerical instabilities when working in low-data modes.
The text was updated successfully, but these errors were encountered:
🚀 Feature Request
Problem: numerical stability for some simulators
Sometimes, SBI gets stuck because of some numerical instability.
Solution
Implements a logit transformation on the conditioning variable bounded within a given interval (min_val, max_val), mapping it to the real line (-inf, inf). This transformation is useful for normalizing bounded data while ensuring numerical stability.
Instead of z_scoring the conditioning variable, one can now also logit-transform it. Allowing in some cases, a more stable evaluation with the rejection-sampling algorithm (if normalized posterior is required).
📌 Additional Context
I have had these issues before with SBI, and implementing a logit transformation helped resolve the numerical instabilities when working in low-data modes.
The text was updated successfully, but these errors were encountered: