Probabilistic Programming

63 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 )

Datasets


Greatest papers with code

Automatic structured variational inference

google-research/google-research 3 Feb 2020

However, the performance of the variational approach depends on the choice of an appropriate variational family.

Probabilistic Programming Variational Inference

Pyro: Deep Universal Probabilistic Programming

uber/pyro 18 Oct 2018

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

Probabilistic Programming Variational Inference

TensorFlow Distributions

tensorflow/probability 28 Nov 2017

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

thu-ml/zhusuan 18 Sep 2017

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

pyro-ppl/numpyro 24 Dec 2019

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

Bayesian Layers: A Module for Neural Network Uncertainty

google/edward2 NeurIPS 2019

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

Gaussian Processes Machine Translation +2

Simple, Distributed, and Accelerated Probabilistic Programming

google/edward2 NeurIPS 2018

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

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

pyprob/pyprob 8 Jul 2019

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

pyprob/pyprob NeurIPS 2019

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