pymc-devs / pymc-devs/nuts-rs

Linear regression example in rust.

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Dominant language
Rust
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Forks
18
Avg merge
5h 31m
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Description

Hello,

I am having trouble understanding how to set up the logp function for my use case, specifically the gradient term. I have managed to implement my model using emcee, as this doesn't require a gradient term but I would really like to use NUTS directly from rust!

A slightly more involved example, along the lines of this would be very helpful to understand how to fit a model (including priors) to data, from which I can extrapolate into my own use case.

Thank you for your work on this excellent crate.

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the crate's logp API and NUTS entry point, then compare the requested example with the linked emcee linear-regression tutorial. The documentation should show a linear model with priors, data, and the gradient term, and demonstrate fitting it with NUTS directly from Rust.

Written by the indexing model from the issue text.

Assessment

Tech stack
rust
Domain
machine-learning
Issue type
Documentation
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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