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Updating GP-Kron notebook #144
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Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
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View / edit / reply to this conversation on ReviewNB OriolAbril commented on 2021-04-24T18:16:09Z Predictions look very different now, it looks like there used to be some kind of diffusion effect that is now gone. Could be due to fonnesbeck commented on 2021-07-03T13:48:51Z The inputs are also different, due to a different seed (which is fine). |
View / edit / reply to this conversation on ReviewNB OriolAbril commented on 2021-04-24T18:16:10Z The |
View / edit / reply to this conversation on ReviewNB OriolAbril commented on 2021-04-24T18:16:10Z Given that pp samples are used to average, each sample in the posterior should be used, not only the first 200 samples from the first chain. I would also suggest using
Rubgy example preview: https://nbviewer.jupyter.org/github/pymc-devs/pymc-examples/blob/main/examples/case_studies/rugby_analytics.ipynb |
View / edit / reply to this conversation on ReviewNB OriolAbril commented on 2021-04-24T18:16:11Z both xarray and theano should be present here too. I added some guidance on adding extra libs in the watermark at https://github.com/pymc-devs/pymc3/wiki/PyMC3-Jupyter-Notebook-Style-Guide |
The inputs are also different, due to a different seed (which is fine). View entire conversation on ReviewNB |
I'm going to merge this, and address Oriol's comments in another PR. |
This pull request addresses issue #71 by updating the Kronecker GP notebook to follow best practices and to include improved descriptions. Some of the plots were also simplified and the plot generating code was cleaned up.