Update to "diagnosing biased inferences"

[fonnesbeck]

Updated to v5

• Notebook follows style guide https://docs.pymc.io/en/latest/contributing/jupyter_style.html
• PR description contains a link to the relevant issue:
  • a tracker one for existing notebooks (tracker issues have the "tracker id" label)
  • or a proposal one for new notebooks
• Check the notebook is not excluded from any pre-commit check: https://github.com/pymc-devs/pymc-examples/blob/main/.pre-commit-config.yaml

---

📚 Documentation preview 📚: https://pymc-examples--699.org.readthedocs.build/en/699/

[review-notebook-app]

Check out this pull request on 

See visual diffs & provide feedback on Jupyter Notebooks.

---

Powered by ReviewNB

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2024-09-12T12:46:29Z
----------------------------------------------------------------

If delta is exactly the same as target_accept better to use the latter.

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2025-01-15T07:43:55Z
----------------------------------------------------------------

This is the correct link to the original post https://mc-stan.org/learn-stan/case-studies/divergences_and_bias.html

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2025-01-15T07:43:56Z
----------------------------------------------------------------

...you can check out Thomas Wiecki's blog post, Why hierarchical models are awesome, tricky, and Bayesian on the same issue.

Clarify notation, the notation is standard for someone that already knows this expresion but can be opaque to someone new to the topic. Also instead of saying " the states of a Markov chain, q0..." maybe say using the posterior samples....

when mentioning split $\hat R$ we could add a reference to the paper (Vehtari, Aki, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner. 2021. “Rank-Normalization, Folding, and Localization: An Improved R̂ for Assessing Convergence of MCMC (with Discussion).”Bayesian Analysis 16 (2): 667–718. https://doi.org/10.1214/20-BA1221.) and/or link to https://arviz-devs.github.io/Exploratory-Analysis-of-Bayesian-Models/Chapters/MCMC_diagnostics.html#hat-r-r-hat

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2025-01-15T07:43:57Z
----------------------------------------------------------------

Do we need the Stan model?

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2025-01-15T07:43:58Z
----------------------------------------------------------------

remove "In the original post a single chain of 1200 sample is applied. However, since split R-hat is not implemented in ArviZ we fit 2 chains with 600 sample each instead."

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2025-01-15T07:43:59Z
----------------------------------------------------------------

az.summary(short_trace, round_to=2)

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2025-01-15T07:44:00Z
----------------------------------------------------------------

az.summary(longer_trace, round_to=2)

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2025-01-15T07:44:00Z
----------------------------------------------------------------

Do we need the Stan model?

[review-notebook-app]

View / edit / reply to this conversation on ReviewNB

aloctavodia commented on 2025-01-15T07:44:01Z
----------------------------------------------------------------

az.summary(fit_ncp80, round_to=2)