Added mcmc_traceplots_unigauss.ipynb Fig 11.14 to 11.17 | Book2
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LGTM. |
Yes Sir, that's a good idea, I'm thinking to create an issue in |
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Line #6. return states, {"states": states, "info": info}
Not clear what info is? Is this something your implementation adds or something provided out of the box by BlackJax?
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Yes, info is returned by blackjax model, I've added comment to clarify it.
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it may be good to add 1-2 lines of comments on why swap axes is needed. Something like -- expected format by arviz is ... and our current format is ...
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Line #15. samples[param] = states.position[param]
for dims > 1, you considered burn-in, but not here.
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Ohh, thanks for pointing out, I've fixed it.
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We initialised prior_alpha in the initial cells and now we're using log_prob_alpha etc. Should we reuse variables?
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Line #1. print(f"Number of divergences (bad prior) = {info.is_divergent[500:,:].sum()}")
What is 500 here? Burn-in? if so, should we create a variable for it?
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I have created burn_in as global variable now
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I agree too! It would be useful for the larger community! I have given some small comments on the notebook. |
Description
Converted numpyro implementation (
notebooks/book2/11/mcmc_traceplots_unigauss_numpyro.ipynb) to blackjax.Please take a note of the following points
Figure Number
Fig 11.14
Fig 11.15
Fig 11.16
Fig 11.17
Figures
1. Fig 11.14
Before PR
After PR
2. Fig 11.15
Before PR
After PR
3. Fig 11.16
Before PR
After PR
4. Fig 11.17
Before PR
After PR
Issue
#890
Checklist
cc: Dr. @murphyk, Prof. @nipunbatra