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Probabilistic Programming

25 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

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

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

Better call Saul: Flexible Programming for Learning and Inference in NLP

COLING 2016 IllinoisCogComp/saul

We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP).

PART-OF-SPEECH TAGGING PROBABILISTIC PROGRAMMING SEMANTIC ROLE LABELING

A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

8 Nov 2018biaslab/ForneyLab.jl

This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms.

PROBABILISTIC PROGRAMMING

Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes

4 Apr 2017probcomp/cgpm

We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.

INFORMATION RETRIEVAL PROBABILISTIC PROGRAMMING

Probabilistic Data Analysis with Probabilistic Programming

18 Aug 2016probcomp/cgpm

This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques.

DIMENSIONALITY REDUCTION PROBABILISTIC PROGRAMMING