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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.

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Papers

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Showing 151–200 of 273 papers

TitleStatusHype
Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming—0
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
Summary - TerpreT: A Probabilistic Programming Language for Program Induction—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
Venture: a higher-order probabilistic programming platform with programmable inference—0
Weighted Programming—0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
WOLFE: An NLP-friendly Declarative Machine Learning Stack—0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
A Compilation Target for Probabilistic Programming Languages—0
A Convenient Category for Higher-Order Probability Theory—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
A Distribution Semantics for Probabilistic Term Rewriting—0
Adversarial Message Passing For Graphical Models—0
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs—0
A Fairness-aware Hybrid Recommender System—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
A New Approach to Probabilistic Programming Inference—0
Anytime Exact Belief Propagation—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
A theory of contemplation—0
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs—0
A Probabilistic Programming Approach To Probabilistic Data Analysis—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
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