描述

I think we could wrap our mcmc parameters in a custom class that would make it easier to work with the mcmc results (in the spirit of rval (?) in posterior package).

In mcmc world (samples)

Scalar -> shape=chain,draw Vector -> shape=chain,draw,vector_dim Matrix -> shape=chain,draw,*matrix_dims

example

When we want to do matrix * vector product with mcmc results, we need to be careful that correct dimensions are used -> Bayesian variable could handle this so the variable would work as any non-mcmc variable.

repr

We don't always need to show all the samples for users but it might be better show some specific statistics (e.g. mean, std)

Our html output could also have other info, rhat/ess (maybe even density picture?)

Similar work can be seen in https://pythonhosted.org/uncertainties/

贡献者指南

技术栈: pythonnumpy
领域: dataanalytics
议题类型: feature
难度: 3
预计时间: 3-5 days
活动状态: stale
清晰度: mostly clear
前置要求: Python programmingMCMC conceptsBasic understanding of arviz
新手友好度: 50
研究方向: Examine the uncertainties package (https://pythonhosted.org/uncertainties/) for a model of wrapping computational results. Look at arviz's current handling of MCMC samples, likely in the InferenceData class (xarray Datasets). Understand the dimension ordering (chain, draw, variable dims). Implement a Bayesian variable class that supports arithmetic operations while preserving sample dimensions. Provide custom repr showing mean, std, and diagnostics like rhat/ess. Discuss with maintainers via the issue for design feedback.