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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 126–150 of 273 papers

TitleStatusHype
String Diagrams with Factorized Densities—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages—0
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs—0
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support—0
Supervised Bayesian Specification Inference from Demonstrations—0
Surrogate Likelihoods for Variational Annealed Importance Sampling—0
Survival prediction and risk estimation of Glioma patients using mRNA expressions—0
Swift: Compiled Inference for Probabilistic Programming Languages—0
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo—0
Tensor Program Optimization with Probabilistic Programs—0
Tensor Variable Elimination for Plated Factor Graphs—0
TerpreT: A Probabilistic Programming Language for Program Induction—0
Testing Probabilistic Circuits—0
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware—0
The Physics of Text: Ontological Realism in Information Extraction—0
The Random Conditional Distribution for Higher-Order Probabilistic Inference—0
Towards an architectural framework for intelligent virtual agents using probabilistic programming—0
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models—0
Transforming Probabilistic Programs for Model Checking—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Uncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming—0
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations—0
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs—0
Using probabilistic programs as proposals—0
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