pymc-devs/pymc

Add core notebook on sample_posterior_predictive for out-of-model predictions

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#6,812 opened on Jul 4, 2023

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Description

Description

We wrote a series of examples for the PyMC labs blogpost: https://www.pymc-labs.io/blog-posts/out-of-model-predictions-with-pymc/

It would be good to write a more technical companion that explains exactly how sample_posterior_predictive works and why it works this way, and when is it valid/not valid to use if for out-of-model predictions.

There is a long thread here that highlights aspects of the current behavior that are unclear / surprising for most users (including developers): https://github.com/pymc-devs/pymc/issues/6047

Note: This is not a beginner friendly issue.

Contributor guide

Research direction
Read the referenced blog post and the long thread (issue #6047) to understand the required explanation. Study the PyMC documentation on sample posterior predictive and the code to gather implementation details. Then outline a tutorial notebook with examples showing valid and invalid use cases for out of model predictions.
Tech stack
python
Domain
machine learning
Issue type
Documentation
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
4
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonBayesian statistics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
25