Tidy and strengthen ar1_bayes lecture#927
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Style and correctness pass on the AR(1) Bayesian posteriors lecture,
aligned with the QuantEcon styleguide:
- Fix bugs: :tags: directive typo (output was never hidden), wrong model
in a summary cell, stationary-variance typo, "kernal", MCMC wording.
- Adopt Unicode Greek variable names, np.random.default_rng, plain N for
the normal distribution, IID, sentence-case headings, {cite:t}, and
ax-style plotting with lw=2.
- Add an Overview section and a roadmap to each section; introduce every
code block with a sentence of prose.
- Link to intro AR(1) lecture; link NUTS explanation to bayes_nonconj
(add a (nuts) anchor there) instead of re-explaining.
- Teach the main point: add a "Comparing the two posteriors" section that
overlays the two posteriors for rho, and pay off the two-library setup
with an explicit PyMC vs NumPyro agreement check.
- Fix terminology once and use it throughout: the "conditioning
assumption" vs the "stationary assumption", defined by whether the
density of y0 depends on the parameters.
- Rewrite the conclusion with a plain-English rule of thumb.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
arviz 1.x (the arviz_plots backend) no longer accepts the figsize kwarg on plot_trace and raises ValueError, breaking notebook execution in CI (which pip-installs the latest arviz). Remove figsize from the two az.plot_trace calls; arviz uses its default size, which is also more consistent with the styleguide. Verified against arviz 1.2.0 that plot_trace(idata), summary(round_to=...), and posterior[...] access all work; only the figsize kwarg was the problem. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This was referenced Jun 22, 2026
📖 Netlify Preview Ready!Preview URL: https://pr-927--sunny-cactus-210e3e.netlify.app Commit: 📚 Changed LecturesBuild Info
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- Restructure the Overview into Setting / Libraries / Imports subsections and move the likelihood factorization down to where it is used. - Explain how the PyMC model is declared (priors, vectorized likelihood as the product of one-step densities) so the setup is less opaque. - Highlight, with notes and code comments, exactly how each assumption is imposed: the conditioning assumption drops f(y0); the stationary assumption restores it via the single y0_obs term. - For NumPyro, add a short PyMC->NumPyro syntax mapping (function vs with block, numpyro.sample, obs= vs observed=) instead of repeating the explanation, and mirror the assumption code comments. - Fix a typo and use "stationary distribution" consistently. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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What
A tidy-up and pedagogical strengthening of the Posterior Distributions for AR(1) Parameters lecture (
ar1_bayes.md), plus a one-line anchor added tobayes_nonconj.mdso the NUTS explanation can be linked rather than duplicated.Changes
Bug fixes
:tag:→:tags:onhide-outputcells (the typo meant install/sampling output was never actually hidden).with AR1_model:to summarisetrace_y0→ corrected toAR1_model_y0.Styleguide alignment
ρ,σ,ε);np.random.default_rnginstead of legacy RNG; plainNfor the normal distribution; "IID"; sentence-case headings;{cite:t}for the in-text Hurwicz citation;ax-style plotting withlw=2and lowercase axis labels.## Overviewsection and a roadmap to each section; every code block is now introduced by a sentence of prose.Teaching improvements
bayes_nonconjinstead of re-explaining it.Note for reviewers
Switching to$y_0=10$ is fixed far in the tail), but please eyeball the rendered preview to confirm the conditioning posterior sits near $0.5$ , the stationary one is pushed toward $1$ , and the two libraries agree.
np.random.default_rngchanges the simulated series (PCG64 vs MT19937), so the figures and posterior numbers will differ from the live site. The qualitative story is preserved (🤖 Generated with Claude Code