pyro-ppl/pyro
Pyro
Pyro is a deep probabilistic programming library and differentiable probabilistic modeler designed for Bayesian inference. It functions as a probabilistic programming language that allows for the construction of complex graphical models using PyTorch tensors and automatic differentiation.
The framework enables the definition of universal probabilistic models as standard Python functions. It integrates deep learning with probabilistic modeling to compute posterior distributions and estimate latent variables through gradient-based optimization and algorithmic solvers.
The system provides a probabilistic inference engine supporting variational and Markov Chain Monte Carlo methods. It handles tensor computation, automatic gradient differentiation, and latent variable tracking to manage the mapping between model parameters and inference updates.
The project includes support for containerized environments and interactive workspace provisioning with hardware acceleration.
Features
- Probabilistic Programming - Provides a language for defining probabilistic models as Python functions for Bayesian inference and uncertainty quantification.
- Automatic Differentiation Frameworks - Provides an engine for computing exact gradients used to optimize variational parameters during inference.
- Posterior Inference - Implements algorithmic solvers for updating prior distributions to calculate posterior distributions for uncertainty quantification.
- Probabilistic Graphical Models - Provides a framework for using graphs to represent and approximate probability distributions over variables.
- Probabilistic Models - Implements statistical models that represent data distributions using probability density functions.
- Differentiable Probabilistic Models - Builds deep universal probabilistic models by leveraging PyTorch for tensor computations and automatic differentiation.
- Differentiable Probabilistic Modeling - Constructs probabilistic models where parameters are optimized through gradient-based methods provided by the tensor engine.
- PyTorch Backends - Leverages PyTorch for tensor operations and GPU acceleration of probabilistic model parameters.
- Bayesian Inference - Calculates posterior distributions and estimates latent variables using Markov Chain Monte Carlo simulations.
- Deep - Combines deep learning with probabilistic modeling to build universal models for statistical analysis.
- PyTorch-Integrated Probabilistic Languages - Defines and performs inference on complex probabilistic graphical models using PyTorch tensors and automatic differentiation.
- Bayesian Probabilistic Programming Frameworks - Provides an integrated software environment for building probabilistic models and performing Bayesian inference.
- Sampling Operation Sequences - Represents graphical models as a sequence of sampling operations that track dependencies for inference algorithms.
- Stochastic Site Registries - Maintains a registry of stochastic sites to manage the mapping between model parameters and inference updates.
- Deep Learning Ecosystems - Probabilistic programming framework for PyTorch.
- Deep Learning Frameworks - Probabilistic programming framework for PyTorch.
- Machine Learning - Deep probabilistic programming with PyTorch.
- Machine-Learning-Frameworks - Probabilistic programming and Bayesian modeling in PyTorch.
- Probabilistic Modeling - Deep Universal Probabilistic Programming.
- PyTorch Repositories - Probabilistic programming library providing tutorials and flow implementations.
