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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 41–50 of 273 papers

TitleStatusHype
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
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains—0
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs—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
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
Differentiable Quantum Programming with Unbounded LoopsCode0
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