PyMC Concepts for Python Interview Preparation

1

Hello all,
I am currently preparing for a Python interview and looking to deepen my understanding of probabilistic programming, specifically using PyMC. Could you please suggest fundamental PyMC topics or concepts crucial for interview readiness? Additionally, any recommended resources or practice problems to reinforce these concepts would be highly valued. I’m exploring resources or practice problems to solidify these concepts.

Also, I have been following this discussion on prediction concepts in PyMC3, but it seems a bit dated. If anyone has more recent updates or guidance relevant to my interview preparation, I’d greatly appreciate your insights.

Thanks in advance for my help :smiling_face:
Furiosa

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Don’t know what your level of knowledge about Bayesian probability is but here is some stuff roughly ordered from easy to harder

For gaining some insight about different types of probability densities and mass functions:

Then for basic stuff on pymc here:
https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/

Beginner level model examples/modelling concepts (with pymc):
https://www.pymc.io/projects/examples/en/latest/blog/category/beginner.html

Cross-validation might be a topic that maybe comes up in an interview (don’t really have much interview experience, don’t take my word for it)?

If you have time before the interview and want to do some reading on Bayesian modelling then

could be a good start (also exists in the books section of core_botebooks link above). This notebook here contains exercises from this book solved in pymc:

https://nbviewer.org/github/cluhmann/DBDA-python/tree/master/Notebooks/

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