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bayes_nonconj: declare the truncated log-normal prior's (0, 1] support (numpyro 0.22) #1065

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Merged
mmcky merged 1 commit into from
Oct 5, 2026
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bayes_nonconj: declare the truncated log-normal prior's (0, 1] support (numpyro 0.22) #1065

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6 changes: 5 additions & 1 deletion lectures/bayes_nonconj.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
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