The Bayesian Inference library for Python R and Julia
Sosa, S.; Brooke McElreath, M.; Ross, C.
Show abstract
O_LIBayesian modeling is a powerful paradigm in modern statistics and machine learning. However, practitioners face significant obstacles in building bespoke models. C_LIO_LIThe landscape of Bayesian software is fragmented across programming languages and abstraction levels. Newcomers often gravitate towards high-level interfaces, like R, in order to use simple generalized linear models (GLMs) through interfaces like brms. C_LIO_LIFor niche problems, researchers must often transition to writing directly in lower-level programming languages, like Stan or JAX, which require specialist knowledge. C_LIO_LIFurthermore, computational demands remain a significant bottleneck, often limiting the feasibility of applying Bayesian methods on large datasets and complex, high-dimensional models. C_LIO_LIThe Bayesian Inference (BI) is a cross-platform software distributed as a Python, R and Julia library. It provides an intuitive model-building syntax with the flexibility of low-level abstraction coding, while also providing pre-built GLM functions. Further, by facilitating hardware-accelerated GPU computation under-the-hood, BI permits high-dimensional models to be fit in a fraction of the time of comparable Stan models (up to 200-fold). C_LI
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