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Probabilistic Programming

31 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.

( Image credit: Michael Betancourt )

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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 REGRESSION

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

Simple, Distributed, and Accelerated Probabilistic Programming

NeurIPS 2018 google/edward2

For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips.

PROBABILISTIC PROGRAMMING

Functional Tensors for Probabilistic Programming

23 Oct 2019pyro-ppl/funsor

It is a significant challenge to design probabilistic programming systems that can accommodate a wide variety of inference strategies within a unified framework.

PROBABILISTIC PROGRAMMING

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

25 Aug 2017pyro-ppl/funsor

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

PROBABILISTIC PROGRAMMING

RankPL: A Qualitative Probabilistic Programming Language

19 May 2017tjitze/RankPL

In this paper we introduce RankPL, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn's ranking theory.

CAUSAL INFERENCE PROBABILISTIC PROGRAMMING

Modular Deep Probabilistic Programming

ICLR 2019 amzn/MXFusion

We demonstrate this idea by presenting a modular probabilistic programming language MXFusion, which includes a new type of re-usable building blocks, called probabilistic modules.

PROBABILISTIC PROGRAMMING