https://github.com/tmbb/ulam_ex

Elixir interface to Stan
https://github.com/tmbb/ulam_ex

Last synced: about 1 month ago
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Elixir interface to Stan

• Host: GitHub
• URL: https://github.com/tmbb/ulam_ex
• Owner: tmbb
• Created: 2023-12-09T23:47:06.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-10-04T19:30:00.000Z (4 months ago)
• Last Synced: 2024-11-12T17:45:56.669Z (2 months ago)
• Language: HTML
• Size: 5.87 MB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-ml-gen-ai-elixir - Ulam - Elixir interface to [Stan](https://mc-stan.org/), a probabilist programming language. (Machine Learning / Traditional Machine Learning)

README

        
# Ulam

Elixir interface to [Stan](https://mc-stan.org/), inspired by

the Python project [CmdStanPy](https://mc-stan.org/cmdstanpy/).

Why should Python programmers have all the Bayesian fun?

## Why?

I'm interested in developping probabilistic programming languages

that compile to Stan. I have found that doing so in Python is pretty

inconvenient. I have decided to give Elixir a try to see how far I can go.

## Installation

The package must be installed from GitHub.

It's not currently stable enough to be uploaded to Hex.

## Examples

Se the example in the tests.

Relevant code:

```elixir

alias Ulam.Stan.StanModel

# Some simple data for the model

data = %{

  N: 10,

  y: [0, 1, 0, 0, 0, 0, 0, 0, 0, 1]

}

# Compile the model from the stan program file

model = StanModel.compile_file("test/stan/models/bernoulli/bernoulli.stan")

# Sample from the model and save it in a dataframe

dataframe =

  StanModel.sample(model, data,

    nr_of_samples: 1000,

    nr_of_warmup_samples: 1000,

    nr_of_chains: 8,

    show_progress_bars: false

  )

```