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bioRxiv · 10.64898/2026.01.19.700318

The Bayesian Inference library for Python R and Julia

Sosa, S.·Brooke McElreath, M.·Ross, C.
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Abstract

O_LIBayesian modeling is a cornerstone of modern ecological and evolutionary research, offering the flexibility to account for hierarchical structures, imperfect detection, and spatial dependencies. However, as ecological datasets grow in scale and complexity--from high-resolution telemetry to phylogenomics--researchers increasingly face a "computational ceiling" where traditional CPUbound inference becomes prohibitively slow. C_LIO_LIThe current software landscape is fragmented. Researchers must often choose between high-level interfaces (e.g., brms in R) that are intuitive but sometimes rigid or slow for massive datasets, and low-level probabilistic programming languages (e.g., Stan, PyMC, JAX) that offer high performance but require specialized programming expertise. C_LIO_LIThis fragmentation is compounded by an "interoperability tax," where code developed in one language (e.g., R) cannot easily leverage the hardware-accelerated backends (GPUs/TPUs) typically found in Python-centric machine learning frameworks. C_LIO_LITo address these issues, we introduce BayesForge(BF), a cross-platform software ecosystem available in Python, R, and Julia. BF provides a unified, intuitive syntax that bridges the gap between ease-of-use and high-performance computation. By leveraging JAX-based backends (NumPyro and TensorFlow Probability), BF enables seamless hardware acceleration under-the-hood. C_LIO_LIWe demonstrate BFs utility through three ecological case studies: social network analysis (Social Relations Model), macroevolutionary uncertainty propagation across posterior tree sets, and latent-variable estimation for vocal repertoires. Benchmarks reveal that BF can achieve up to a 270-fold speedup over Stan implementations for large-scale networks, transforming weeks of computation into minutes and enabling more robust, uncertainty-aware ecological inference. C_LI

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Sosa, S., Brooke McElreath, M., Ross, C.. 2026-01-22. The Bayesian Inference library for Python R and Julia. https://doi.org/10.64898/2026.01.19.700318

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