Thanks for clarifying your comment, Jesse!
I’ve cleaned up my code, which I started using to learn PyMC, and am attaching it here with the input data. I tested it on my Mac and Colab, so hopefully, it should work without issues.
A few notes:
- I use the Latent model as a default (i.e.,
MODEL = 'GP_Latent_TS') based on the comments I received here. - I just used a few hundred samples for testing purposes in this slow model – this needs to be adjusted.
- I have two options for priors for my state vector “Lambda”: truncated normal and lognormal. I find that the lognormal prior yields an unrealistic posterior value, likely due to the long-tailed distribution. But I don’t know how to set the bounds for lognormal. If you have any ideas, please let me know.
- I added some plots; I guess they will show what I am trying to see as the final result, which is the median Lambda values and their uncertainty.
- Given the inherent error in my physical model in producing the
Kmatrix, GP may not capture all of the observations. - If you see any mistakes or have any comments or suggestions, your help would be appreciated.
I’m still not sure about the posterior predictive sampling (i.e., sample_posterior_predictive) for my positive observation Y, although I may not need it. Then what would be used for model checking?
I couldn’t attach my Jupyter Notebook file (not allowed?), so I converted it to a Python script. On Visual Studio Code, I could run the whole script after pasting it into a single Jupyter Notebook cell. I guess you know it better than I do …
Again, thank you so much for your help!
– Seongeun
GP.py (10.4 KB)