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🌐 [translation-sync] bayes_nonconj: declare the truncated log-normal prior's (0, 1] support (numpyro 0.22) #288

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Merged
mmcky merged 2 commits into from
Oct 6, 2026
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🌐 [translation-sync] bayes_nonconj: declare the truncated log-normal prior's (0, 1] support (numpyro 0.22) #288

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c05bf33
Update translation: lectures/bayes_nonconj.md
mmcky Oct 5, 2026
6f627a5
Update translation: .translate/state/bayes_nonconj.md.yml
mmcky Oct 5, 2026
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8 changes: 4 additions & 4 deletions .translate/state/bayes_nonconj.md.yml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
source-sha: b78fbcddae98a645bff0f01bb28a1e7955db5f53
synced-at: "2026-07-18"
source-sha: d9caa8174ac100d375f2a0742b47fe6326b229ac
synced-at: "2026-10-05"
model: claude-sonnet-5
mode: RESYNC
mode: UPDATE
section-count: 6
tool-version: 0.17.0
tool-version: 0.29.3
8 changes: 6 additions & 2 deletions lectures/bayes_nonconj.md
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Expand Up @@ -4,7 +4,7 @@ jupytext:
extension: .md
format_name: myst
format_version: 0.13
jupytext_version: 1.16.4
jupytext_version: 1.17.2
kernelspec:
display_name: Python 3 (ipykernel)
language: python
Expand Down Expand Up @@ -354,7 +354,11 @@ NumPyro通过让`TruncatedNormal`经过`ExpTransform`来构造这个分布。
def truncated_lognormal(μ, σ):
"截断到单位区间(0, 1]的对数正态分布。"
base = dist.TruncatedNormal(loc=μ, scale=σ, low=-jnp.inf, high=0.0)
return dist.TransformedDistribution(base, dist.transforms.ExpTransform())
# 声明(0, 1]这个支撑范围：单独使用ExpTransform会声明支撑为(0, ∞)，
# 这会让采样器提议出θ > 1的取值
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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