Bayesian additive regression trees for probabilistic programming

Q: How reproducible is "Bayesian additive regression trees for probabilistic programming"?

Estimated time to first reproduction: a few days. Risk flags: Adjacent implementations are not paper-verified. No maintained paper-verified implementation was found; start with the closest related repositories below.

Miriana Quiroga, Pablo Garay, Juan Manuel Alonso, Juan Martín Loyola, Osvaldo A. Martin

Published: Jun 8, 2022

No direct implementation yet

Evidence: Adjacent

Domain fit: AI-adjacent

Verified repos: 0

Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

Time to first repro: a few days

1 risk flag

DOI Publisher

Bayesian additive regression trees (BART) is a non-parametric method to approximate functions. It is a black-box method based on the sum of many trees where priors are used to regularize inference, mainly by restricting trees' learning capacity so that no individual tree is able to explain the data, but rather the sum of trees. We discuss BART in the context of probabilistic programming languages (PPL), i.e., we pres ...

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ent BART as a primitive that can be used as a component of a probabilistic model rather than as a standalone model. Specifically, we introduce the Python library PyMC-BART, which works by extending PyMC, a library for probabilistic programming. We showcase a few examples of models that can be built using PyMC-BART, discuss recommendations for the selection of hyperparameters, and finally, we close with limitations of our implementation and future directions for improvement.

Technical details

Canonical key: doi-10.48550_arxiv.2206.03619

Cache status: Stale (SWR served)

Generated at: Jun 18, 2026, 3:00 PM

Artifact coverage: sparse

HF provider: ok (mixed)

PWC source used: No

LLM status: not_generated

LLM model: n/a

LLM generated: Unknown

LLM content type: n/a

HF policy: hf-relevance-v27

context only

Benchmarks: thin evidence

Time to repro: a few days

1 risk flag

Results & Benchmarks

Freshness tier: cold

Direct + Inferred Evidence

Some benchmark signal exists in the extracted evidence, but it is not structured strongly enough yet for a confident benchmark decision.

Bayesian additive regression trees (BART) is a non-parametric method to approximate functions.

Implementation Evidence Summary

Confidence: medium

prakhar1989/awesome-courses is the closest maintained adjacent implementation (Matches contextual method/domain keyword: computer science). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 69130 GitHub stars.

Reproduction Risks

Adjacent implementations are not paper-verified
Recommended repository is adjacent and not paper-verified.

Hardware Notes

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Evidence disclosure

Evidence graph: 3 refs, 3 links.

Utility signals: depth 100/100, grounding 85/100, status high.

Implementation Status

No verified maintained repo

There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.

No maintained paper-verified implementation was found; start with the closest related repositories below.
Compare repo methods against the paper equations/algorithm before trusting metrics.
Create a minimal baseline implementation from the paper and use adjacent repos as references.

Time to first repro: a few days

Reproduction readiness

No Repo

Time to first repro: days

Last checked: Jun 18, 2026

Hardware requirements

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

No verified implementation available

· No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

Closest related implementations

These are not paper-verified. Use them as reference points when no direct implementation is available.

prakhar1989/awesome-courses

Adjacent

Confidence: Medium

Stars: 69,130

Matches contextual method/domain keyword: computer science

Hugging Face artifacts

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Models

Bayesian inference Probabilistic logic Bayesian inference model

Datasets

Probabilistic logic dataset Bayesian inference benchmark Bayesian inference dataset

Spaces

Probabilistic logic demo Bayesian inference gradio Bayesian inference demo

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Research context

Citations

References

Tasks

Computer science, Probabilistic logic, Hyperparameter, Python (programming language), Prior probability, Physical Sciences

Methods

Bayesian inference, Bayesian probability

Domains

Machine learning, Artificial intelligence

Evaluation & Human Feedback Data

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Computer science Probabilistic logic Hyperparameter Python (programming language) Prior probability Physical Sciences