diff --git a/lectures/bayes_nonconj.md b/lectures/bayes_nonconj.md
index c70df051d..995408317 100644
--- a/lectures/bayes_nonconj.md
+++ b/lectures/bayes_nonconj.md
@@ -332,7 +332,11 @@ NumPyro builds this by feeding a `TruncatedNormal` through an `ExpTransform`.
 def truncated_lognormal(μ, σ):
     "Log-normal distribution truncated to the unit interval (0, 1]."
     base = dist.TruncatedNormal(loc=μ, scale=σ, low=-jnp.inf, high=0.0)
-    return dist.TransformedDistribution(base, dist.transforms.ExpTransform())
+    # Declare the (0, 1] support: ExpTransform alone advertises (0, ∞),
+    # which would let the sampler propose θ > 1.
+    class _UnitLogNormal(dist.TransformedDistribution):
+        support = dist.constraints.interval(0.0, 1.0)
+    return _UnitLogNormal(base, dist.transforms.ExpTransform())
 
 prior_ln = truncated_lognormal(0.0, 1.0)
 mcmc_ln = run_nuts(binomial_model, prior_ln, k, n)