Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with these key principles: Universal: Pyro can represent any computable probability distribution. Scalable: Pyro scales to large data sets with little overhead. Minimal: Pyro is implemented with a small core of powerful, composable abstractions. Flexible: Pyro aims for automation when you want it, control when you need it.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
- Alexandrov, Alexander; Benidis, Konstantinos; Bohlke-Schneider, Michael; Flunkert, Valentin; Gasthaus, Jan; Januschowski, Tim; Maddix, Danielle C.; Rangapuram, Syama; Salinas, David; Schulz, Jasper; Stella, Lorenzo; Türkmen, Ali Caner; Wang, Yuyang: GluonTS: probabilistic and neural time series modeling in Python (2020)
- Drori, Iddo: Deep variational inference (2020)
- Hillerström, Daniel; Lindley, Sam; Atkey, Robert: Effect handlers via generalised continuations (2020)
- Mario Morvan, Angelos Tsiaras, Nikolaos Nikolaou, Ingo P. Waldmann: PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch (2020) arXiv
- Bingham, Eli; Chen, Jonathan P.; Jankowiak, Martin; Obermeyer, Fritz; Pradhan, Neeraj; Karaletsos, Theofanis; Singh, Rohit; Szerlip, Paul; Horsfall, Paul; Goodman, Noah D.: Pyro: deep universal probabilistic programming (2019)
- Kumar, R.; Colin, C.; Hartikainen, A.; Martin, O. A.: ArviZ a unified library for exploratory analysis of Bayesian models in Python. (2019) not zbMATH
- Guillaume Baudart, Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar: Yaps: Python Frontend to Stan (2018) arXiv