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### What are Probabilistic Programming Languages?

**What are Probabilistic Programming Languages?**

- Universal: Pyro is a universal PPL - it can represent any computable probability distribution. How? By starting from a universal language with iteration and recursion (arbitrary Python code), and then adding random sampling, observation, and inference.
- Scalable: Pyro scales to large data sets with little overhead above hand-written code. How? By building modern black box optimization techniques, which use mini-batches of data, to approximate inference.
- Minimal: Pyro is agile and maintainable. How? Pyro is implemented with a small core of powerful, composable abstractions. Wherever possible, the heavy lifting is delegated to PyTorch and other libraries.
- Flexible: Pyro aims for automation when you want it and control when you need it. How? Pyro uses high-level abstractions to express generative and inference models while allowing experts to easily customize inference.

- Rich modeling language" Support for univariate and multivariate variables, both continuous and discrete. Models can be constructed from a broad range of factors including arithmetic operations, linear algebra, range and positivity constraints, Boolean operators, Dirichlet-Discrete, Gaussian, and many others.
- Multiple inference algorithms" Built-in algorithms include Expectation Propagation, Belief Propagation (a special case of EP), Variational Message Passing and Gibbs sampling.
- Designed for large-scale inference: Infer.NET compiles models into inference source code which can be executed independently with no overhead. It can also be integrated directly into your application.
- User-extendable: Probability distributions, factors, message operations, and inference algorithms can all be added by the user. Infer.NET uses a plug-in architecture which makes it open-ended and adaptable.