pymc-devs/pymc

Implement `entropy` dispatches for distributions

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#8,085 opened on Feb 3, 2026

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feature requesthelp wantedrequest discussion

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Description

Description

#7530 added a helper to compute the closed-form mean of a distribution, and #8084 adds variance.

One last useful helper that pops up in applications is the entropy. It's used in ML as a regularization term (e.g. in reinforment learning models that learn a policy function like DDPG), and can be used to pick priors (e.g. in pz.maxent.

It could be nice to have these around. I'm marking as request discussion because I realize by adding all these different moments and statistics we're going down a bit of a rabbit hole, and maybe someone wants to step in and say stop now.

Contributor guide

Research direction
Study existing mean and variance dispatches in the codebase (e.g., #7530 and #8084) to understand the pattern, then implement entropy for each distribution using available closed form formulas or numerical methods.
Tech stack
python
Domain
machine learningdata
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
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
PythonPyMCbasic knowledge of probability distributions
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
65