arviz-devs/arviz

Bayesian variable (one-object mode)

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#1668 opened on Apr 17, 2021

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DiscussionEnhancementFeature RequestHelp WantedWorkflowxarray

Description

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/

Contributor guide

Tech stack
pythonnumpy
Domain
dataanalytics
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
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.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
Python programmingMCMC conceptsBasic understanding of arviz
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50
Research direction
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.