What is Pyro?
Pyro is a universal probabilistic programming language (PPL) developed in Python, utilizing PyTorch as its backend framework. It empowers users to create flexible and expressive deep probabilistic models by integrating modern deep learning techniques with Bayesian modeling principles. The design emphasizes universality, allowing representation of any computable probability distribution, and scalability for handling large datasets efficiently.
Built with a minimal core of composable abstractions, Pyro offers flexibility, providing automation where desired and control when necessary. It is complemented by NumPyro, a NumPy-backed version using JAX for enhanced performance, particularly for algorithms like HMC and NUTS. Pyro is an open-source project available under the Apache 2.0 License.
Features
- Universal: Represents any computable probability distribution.
- Scalable: Handles large datasets with minimal overhead.
- Minimal Core: Built with powerful, composable abstractions.
- Flexible: Offers automation and user control.
- PyTorch Backend: Leverages PyTorch for computation.
- NumPyro Integration: Includes a NumPy-backed version using JAX for accelerated performance.
- Open Source: Available under Apache 2.0 License.
Use Cases
- Developing complex Bayesian models.
- Implementing deep generative models.
- Performing probabilistic inference on large datasets.
- Research in machine learning and artificial intelligence.
- Building custom probabilistic models integrated with deep learning components.
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