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+# Using the logit transformation
+If you've ruled out simulator issues, you can try
+training your density or ratio estimator in an unbounded space
+using a logit transformation:
+
+- **For NPE**: The transformation maps bounded parameters θ
+to unbounded space before training, then applies the inverse (sigmoid)
+after training to ensure posterior samples stay within prior bounds.
+
+- **For NLE/NRE**: The transformation would need to map bounded
+data x to unbounded space, which requires estimating data bounds
+from simulations (more complex).
+
+To enable this for NPE:
+
+```python
+density_estimator_build_fun = posterior_nn(
+    model="zuko_nsf",
+    hidden_features=60,
+    num_transforms=3,
+    z_score_theta="transform_to_unconstrained"  # Transforms parameters to unconstrained space
+    x_dist=prior  # For NPE, this specifies bounds for parameters (internally called 'x')
+)
+inference = NPE(prior, density_estimator=density_estimator_build_fun)
+```
+
+This ensures that your density estimator operates in a
+transformed space where it respects prior bounds,
+improving the efficiency of rejection sampling.
+
+Note: The `x_dist=prior` might seem confusing - internally,
+sbi uses generic `x,y` notation where for NPE, `x` represents
+parameters (θ) and `y` represents data.
+This is why we pass the prior as `x_dist`.
+
+Important:
+
+- This transformation is currently only supported for zuko density estimators.
+- For **NLE/NRE**, setting up this transformation is more
+complex as it requires estimating bounds for the simulated data
+rather than using prior bounds.