borodark/eXMC
Probabilistic programming in BEAM, inference with a pulse
This project helps data scientists, statisticians, and researchers build and run advanced statistical models, particularly Bayesian models. You define your model using a clear syntax, input your observed data, and the system outputs posterior samples and diagnostics that help you understand the underlying patterns and make predictions. It's designed for practitioners who need robust statistical inference and want to leverage concurrent processing for faster, more reliable results.
Use this if you are building complex statistical models and need a system that can run multiple simulations simultaneously, provide real-time updates on your model's progress, and gracefully handle errors in individual simulations.
Not ideal if you primarily work with frequentist statistics or if your existing Python-based Bayesian workflow is sufficient without requiring advanced concurrency or distributed processing.
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9
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Language
Elixir
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Category
Last pushed
Feb 21, 2026
Commits (30d)
0
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