# Probabilistic Programming

85 papers with code • 0 benchmarks • 0 datasets

Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.

( Image credit: Michael Betancourt )

## Benchmarks

These leaderboards are used to track progress in Probabilistic Programming
## Libraries

Use these libraries to find Probabilistic Programming models and implementations## Most implemented papers

# TensorFlow Distributions

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.

# Automatic Differentiation Variational Inference

Probabilistic modeling is iterative.

# Inference Compilation and Universal Probabilistic Programming

We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods.

# Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization.

# Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.

# An Introduction to Probabilistic Programming

We start with a discussion of model-based reasoning and explain why conditioning is a foundational computation central to the fields of probabilistic machine learning and artificial intelligence.

# Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.

# Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro

NumPyro is a lightweight library that provides an alternate NumPy backend to the Pyro probabilistic programming language with the same modeling interface, language primitives and effect handling abstractions.

# Scenic: A Language for Scenario Specification and Scene Generation

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.

# Sinkhorn AutoEncoders

We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder aggregated posterior and the prior in latent space, plus a reconstruction error.