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methodology-11

Probabilistic Programming

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

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2 papers 663

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Most implemented papers

TensorFlow Distributions

M. Hoffman, I. Langmore, Dustin Tran, Joshua V. Dillon, E. Brevdo, R. Saurous, Alexander A. Alemi, Srinivas Vasudevan, David A. Moore, Brian Patton•Mon Nov 27 2017

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation and enables modular construction of high dimensional distributions and transformations not possible with previous libraries.

380

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

pyro-ppl/funsor

2 papers 230

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Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

Thomas Bo Schön, Lawrence M. Murray, Daniel Lundén, J. Kudlicka, David Broman•Thu Aug 24 2017

A dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs is introduced, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators.

61 0

Automatic Differentiation Variational Inference

R. Ranganath, Dustin Tran, D. Blei, A. Kucukelbir, A. Gelman•Tue Mar 01 2016

Automatic differentiation variational inference (ADVI) is developed, where the scientist only provides a probabilistic model and a dataset, nothing else, and the algorithm automatically derives an efficient Variational inference algorithm, freeing the scientist to refine and explore many models.

771 0

Inference Compilation and Universal Probabilistic Programming

A. G. Baydin, T. Le, Frank D. Wood•Fri Sep 30 2016

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

145 0

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

Prabhat, A. G. Baydin, Gilles Louppe, Lei Shao, Lukas Heinrich, W. Bhimji, Andreas Munk, Bradley Gram-Hansen, K. Cranmer, Frank D. Wood•Thu Jul 19 2018

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 is presented.

32 0

An Introduction to Probabilistic Programming

Frank Wood, Brooks Paige, Jan-Willem van de Meent, Hongseok Yang•Wed Sep 26 2018

This document starts with a discussion of model-based reasoning and explains why conditioning as a foundational computation is central to the fields of probabilistic machine learning and artificial intelligence, and introduces a simple first-order Probabilistic programming language whose programs define static-computation-graph, finite-variable-cardinality models.

218 0

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Prabhat, A. G. Baydin, Philip H. S. Torr, Gilles Louppe, Lawrence Meadows, Lei Shao, Lukas Heinrich, W. Bhimji, Andreas Munk, Bradley Gram-Hansen, K. Cranmer, Frank D. Wood, Jialin Liu, Saeid Naderiparizi, Mingfei Ma, Xiaohui Zhao, V. Lee•Sun Jul 07 2019

A novel PPL framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol and provides Markov chain Monte Carlo (MCMC) and deep-learning-based inference compilation (IC) engines for tractable inference is presented.

62 0

Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro

M. Jankowiak, Neeraj Pradhan, Du Phan•Sun Sep 15 2019

The power of composing Pyro's effect handlers with the program transformations that enable hardware acceleration, automatic differentiation, and vectorization in JAX are demonstrated.

450 0

Scenic: a language for scenario specification and scene generation

Xiangyu Yue, Daniel J. Fremont, T. Dreossi, Shromona Ghosh, A. Sangiovanni-Vincentelli, S. Seshia•Mon Sep 24 2018

A domain-specific language, Scenic, is designed for describing scenarios that are distributions over scenes, as a probabilistic programming language, that allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene.

295 0

Sinkhorn AutoEncoders

M. Welling, Rianne van den Berg, Samarth Bhargav, Giorgio Patrini, M. Carioni, Patrick Forr'e, Tim Genewein, F. Nielsen•Mon Jul 15 2019

It is proved that optimizing the encoder over any class of universal approximators, such as deterministic neural networks, is enough to come arbitrarily close to the optimum, and advertised this framework, which holds for any metric space and prior, as a sweet-spot of current generative autoencoding objectives.

104 0

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Probabilistic Programming | State-of-the-Art