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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 51–75 of 273 papers

TitleStatusHype
Diffusion models for probabilistic programmingCode0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Inferring Capabilities from Task Performance with Bayesian Triangulation—0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming—0
From Probabilistic Programming to Complexity-based Programming—0
Towards an architectural framework for intelligent virtual agents using probabilistic programming—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Bayesian Calibration of MEMS AccelerometersCode0
Automating Model Comparison in Factor GraphsCode0
String Diagrams with Factorized Densities—0
Dimensionality Reduction as Probabilistic Inference—0
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black BoxCode0
Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving—0
Neural Probabilistic Logic Programming in Discrete-Continuous Domains—0
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs—0
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains—0
Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework—0
Automatically Marginalized MCMC in Probabilistic ProgrammingCode0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
Differentiable Quantum Programming with Unbounded LoopsCode0
Ice Core Dating using Probabilistic ProgrammingCode0
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Robust leave-one-out cross-validation for high-dimensional Bayesian modelsCode0
Borch: A Deep Universal Probabilistic Programming LanguageCode0
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