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Probabilistic Programming

38 papers with code · Methodology
Subtask of Bayesian Inference

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.

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Greatest papers with code

Pyro: Deep Universal Probabilistic Programming

18 Oct 2018uber/pyro

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research.

PROBABILISTIC PROGRAMMING

TensorFlow Distributions

28 Nov 2017tensorflow/probability

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

PROBABILISTIC PROGRAMMING

ZhuSuan: A Library for Bayesian Deep Learning

18 Sep 2017thu-ml/zhusuan

In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.

PROBABILISTIC PROGRAMMING

Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro

24 Dec 2019pyro-ppl/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.

PROBABILISTIC PROGRAMMING

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

NeurIPS 2019 pyprob/pyprob

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.

PROBABILISTIC PROGRAMMING

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

8 Jul 2019pyprob/pyprob

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

PROBABILISTIC PROGRAMMING

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

NeurIPS 2019 pyprob/pyprob

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.

PROBABILISTIC PROGRAMMING

Inference Compilation and Universal Probabilistic Programming

31 Oct 2016pyprob/pyprob

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.

PROBABILISTIC PROGRAMMING

Bayesian Layers: A Module for Neural Network Uncertainty

NeurIPS 2019 google/edward2

We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty.

GAUSSIAN PROCESSES MACHINE TRANSLATION PROBABILISTIC PROGRAMMING

Bayesian Layers: A Module for Neural Network Uncertainty

NeurIPS 2019 google/edward2

We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty.

GAUSSIAN PROCESSES MACHINE TRANSLATION PROBABILISTIC PROGRAMMING