Pyro is an open-source probabilistic programming library built on Pytorch. Developed by Uber AI Labs, Pyro is designed to enable the development of scalable and flexible probabilistic models. It is tailored for applications in probabilistic machine learning, Bayesian inference, and statistical modeling, making it suitable for both academic research and production environments.

• Pyro combines the power of probabilistic programming with PyTorch’s computational efficiency, allowing users to define complex models with hierarchical structures, dynamic data, and non-standard distributions.
• Models can seamlessly integrate deep learning components, enabling advanced tasks such as uncertainty quantification and Bayesian neural networks.

• Pyro supports a variety of inference techniques, including:
  • Variational Inference: Efficient approximation methods for complex posterior distributions.
  • Markov Chain Monte Carlo (MCMC): For exact inference in smaller-scale applications.

• Built on PyTorch, Pyro leverages dynamic computation graphs, providing the flexibility to handle complex probabilistic reasoning in neural networks and other machine learning models.

• Pyro uses PyTorch’s automatic differentiation to optimize model parameters efficiently, making it easier to implement and train sophisticated models.

• Pyro Documentation: Comprehensive examples and guides for building probabilistic models.
• Getting Started: A beginner-friendly tutorial series.

• GitHub Repository: Access source code, contribute, and track issues.

• PyMC: An alternative probabilistic programming library for Bayesian modeling.
• Stan: A platform for statistical modeling and high-performance inference.
• TensorFlow Probability: For probabilistic programming with TensorFlow.

• Probabilistic Programming and Bayesian Methods for Hackers: A hands-on introduction to probabilistic modeling.
• Bayesian Analysis with Python: A practical guide to Bayesian modeling.