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ArviZ 2021 roadmap

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Osvaldo Martin edited this page Jan 19, 2021 · 17 revisions

Roadmap topics for 2021

TFP

  • Clean up TFP interface for current version and TensorFlow 2
  • Provide a "default trace function" that will extract the metrics arviz expects
  • Provide example of producing a fully-featured InferenceData object

Plots

  1. half-eye, dots, see ggdist https://mjskay.github.io/ggdist/
    • Better than violin, smaller, and there are better variations
  2. Dot plots
  • Useful if people need to infer talk probabilities
  1. Calibration plot for classification, see. e.g. https://avehtari.github.io/modelselection/diabetes.html
    • Multiclass would be like a pair plots
  2. ecdf / ecdf-difference with correct envelopes (more info soon in hopefully Jan)
  • loo-pit has further issues with ecdf
  • Can be used in convergence diagnostics rater than rank plot
  1. Add a helper function to easily stack the chain and draws dimensions into a sample dimension, instead of having to do idata.posterior.stack(sample=("chain", "draw")). Often useful when you don't care about which chain a draw is coming from. Issue to track this goal here.

Public API Stability

  • Duplicity of plot_dist and plot_kde
  • plot_hdi draws and samples conversion
  • Plotting API is not good, input and output still a mess
    • Inconsistency in changing things in plots
    • Are input arguments are all the same

Lots of chain plots

  • TFP is creating more chains than samples in some cases
  • Could help with simulation based calibration
  • Rhat computation changes based on short chains versus long chains

Refitting

  • reloo + iwmm? (Importance weighted moment matching in R Loo package and supported by BRMS)
    • Can improve results compared to psis loo with less computation time, doesn't always work
    • Can save time for people, but requires that were using the model again
    • https://arxiv.org/abs/1906.08850

Julia

  • Getting converter for gen.jl
  • Nice to have: Patch in julia's package as a backend
    • Python needs functionality to patch in backend

Proj pred R Package

  • Determine where this would go in python package world (this seems like a work for Bambi)

Generic Plotting Backend

  • Let people bring their own backend and let ArviZ do all the hard math stuff for them

DEI Outreach

  • Make sure we do a couple DEI
    • Use NumFOCUS money

Exploratory Analysis Repo

  • Held up on paying people to finish that. Otherwise its a big opportunity cost

Inference Data to and from R

  • A way to transfer 1 to 1 mapping from ArviZ to posterior and back
  • Need to discuss with posterior devs to see what the best way would be to do this
  • https://mc-stan.org/posterior/

Social

Funds

  • NASA Roses Grant
  • Can come up with more precise
  • Could pay for developers and DEI

ArviZCon? ArviZ social event?

  • Assume its going to be Finland

Google Summer of Code

  • Definitely

Google Summer of Docs

  • Will for this one

Onboard contributors

  • 2 or 3 new insular
    • Another jl dev if we do generic plotting backend
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