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Pyro is a flexible, universal probabilistic programming language (PPL) built on PyTorch. It allows for expressive deep probabilistic modeling, combining the best of modern deep learning and Bayesian modeling. Pyro is centered on four main principles: Universal, Scalable, Minimal and Flexible. Pyro is universal in that it can represent any computable probability distribution. It scales easily to large datasets with minimal overhead, and has a small yet powerful core of composable...

...Libraries from Python, R, C/Fortran, C++, and Java can also be used.

Cobalt is a lightweight, experimental, fully object-oriented programming language built using Python. It is designed for learning programming concepts, language design, and rapid prototyping. Cobalt provides support for all major OOP features, including classes, objects, inheritance, polymorphism, abstraction, encapsulation, constructors, and reusable modular code design. The language uses a custom interpreter that performs lexical analysis, parsing, and runtime execution. ...

Pipelines is a language and runtime for crafting massively parallel pipelines. Unlike other languages for defining data flow, the Pipeline language requires the implementation of components to be defined separately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline while providing access to thousands of active libraries for machine learning, data analysis, and processing.

A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming.