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NumPyro documentation — NumPyro documentation
Explore NumPyro's documentation for building and running probabilistic models in Python with fast, flexible Bayesian inference and detailed examples.
Build and test Bayesian models in Python easily
NumPyro's documentation site is your go-to resource for learning how to build and run probabilistic models in Python. Whether you're new to Bayesian inference or an experienced machine learning practitioner, you'll find detailed guides, examples, and references to help you get started and go deeper.
You can browse step-by-step tutorials, explore advanced examples like time series forecasting and variational autoencoders, and understand the nuts and bolts of probabilistic programming with NumPyro. The site also covers key concepts such as distributions, inference, and effect handlers, making it easier to model complex data and run efficient computations.
This documentation is designed for data scientists, researchers, and developers who want to leverage NumPyro's power for fast, flexible Bayesian modeling. With clear explanations and practical code samples, it's a valuable companion for anyone working in probabilistic machine learning or statistical modeling.
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