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Probabilistic Programming

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 )

Papers

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Showing 76–100 of 273 papers

TitleStatusHype
Inference Compilation and Universal Probabilistic ProgrammingCode0
Functional Tensors for Probabilistic ProgrammingCode0
Joint Distributions for TensorFlow ProbabilityCode0
Automatic Reparameterisation of Probabilistic ProgramsCode0
Accelerating Metropolis-Hastings with Lightweight Inference CompilationCode0
Automatic structured variational inferenceCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyroCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Hamiltonian Monte Carlo for Probabilistic Programs with DiscontinuitiesCode0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
Machine Teaching of Active Sequential LearnersCode0
Diffusion models for probabilistic programmingCode0
Robust leave-one-out cross-validation for high-dimensional Bayesian modelsCode0
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric BayesCode0
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming—0
Compartmental Models for COVID-19 and Control via Policy Interventions—0
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching—0
A Step from Probabilistic Programming to Cognitive Architectures—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
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