pymc-devs/pymc

Implement `entropy` dispatches for distributions

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#8,085 创建于 2026年2月3日

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 (11 评论) (0 反应) (0 负责人)Python (7,926 star) (1,902 fork)batch import
feature requesthelp wantedrequest discussion

描述

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.

贡献者指南

技术栈
python
领域
machine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
half day
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
active
清晰度议题是否清楚说明期望改动、验收标准和下一步。
mostly clear
前置要求
Read #7530 and #8084 for patternBasic understanding of PyMC distributionsKnowledge of entropy for probability distributions
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
40
研究方向
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.