pymc-devs/pymc

Implement `entropy` dispatches for distributions

Open

#8,085 opened on 2026年2月3日

GitHub で見る
 (11 comments) (0 reactions) (0 assignees)Python (7,926 stars) (1,902 forks)batch import
feature requesthelp wantedrequest discussion

説明

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.

コントリビューターガイド

技術スタック
python
領域
machine learning
Issue 種別
feature
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
3
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
half day
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
active
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
mostly clear
前提条件
Read #7530 and #8084 for patternBasic understanding of PyMC distributionsKnowledge of entropy for probability distributions
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
40
調査方針
Review #7530 and #8084 for the implementation pattern of mean and variance dispatches. Examine the current dispatch system in PyMC's distributions module, likely in `pymc/distributions/dispatches.py`. The discussion in the issue may clarify which distributions need entropy and how to compute closed form entropy. Ensure the implementation follows the established pattern and includes tests.