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SCHOLARLY PUBLICATION ✓ Open Access

Pyro: Deep Universal Probabilistic Programming

Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz H. Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Kumar Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman

📖 arXiv (Cornell University) 📅 2018-10-18 🔗 DOI: 10.48550/arxiv.1810.09538

📄 Abstract

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.

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