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feat(recipes): add Turing Bayesian probabilistic programming library#288

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feat(recipes): add Turing Bayesian probabilistic programming library#288
jordanrule wants to merge 4 commits into
modular:mainfrom
jordanrule:main

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@jordanrule

@jordanrule jordanrule commented Jun 22, 2026

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Overview

This PR adds the Turing library to the Modular community channel. Turing is a focused Mojo library for Bayesian probabilistic programming with HKT-style type safety and practical inference engine paths.

Package Details

Key Features

  • HKT-inspired type design: Prevents invalid signal composition at compile time through semantic type tagging
  • Multiple inference paths: MAP, Metropolis-Hastings, Sequential Monte Carlo, and Variational Inference
  • Mojo compile-system integration: Target-agnostic model semantics with explicit CPU/GPU lowering behavior
  • Practical applications: Finance (risk scoring), Healthcare (triage), Operations (forecasting), and Recommendations (feed ranking)

What's Included

  • Core library: src/hkt_probprog.mojo with HKT-inspired inference engine
  • Examples: Social recommendation demo showing library usage with Bluesky
  • Tests: Comprehensive tests for type safety and algorithm correctness

Checklist

  • recipe.yaml specifies MAX/Mojo version compatibility (compatible with mojo-compiler, no version constraint)
  • License file is packaged (LICENSE file present in repository)
  • Build number set to 0 (first submission)
  • Package structure follows Modular community standards

Related Links

Testing

The recipe includes automated tests that verify:

  1. HKT type system correctness (test_hkt_demo.mojo)
  2. Inference engine path behavior across all algorithm variants (test_inference_paths.mojo)

Logo

Logo included as image.jpeg - purple infinity symbol representing probabilistic computing and Bayesian inference.

@jordanrule

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hi @carolinefrasca - this should be ready to re-run in CI. apologies for delay, learning process on my end!

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