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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 101–150 of 273 papers

TitleStatusHype
Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving—0
Proceedings 37th International Conference on Logic Programming (Technical Communications)—0
Proceedings 38th International Conference on Logic Programming—0
Proceedings 40th International Conference on Logic Programming—0
Program Analysis of Probabilistic Programs—0
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models—0
Querying Labeled Time Series Data with Scenario Programs—0
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation—0
RankPL: A Qualitative Probabilistic Programming Language—0
Recalibrating classifiers for interpretable abusive content detection—0
RELLY: Inferring Hypernym Relationships Between Relational Phrases—0
Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups—0
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach—0
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints—0
Simple, Distributed, and Accelerated Probabilistic Programming—0
Simulation-based inference methods for particle physics—0
Simulation Intelligence: Towards a New Generation of Scientific Methods—0
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming—0
Slice Sampling for Probabilistic Programming—0
Representation of Molecules via Algebraic Data Types : Advancing Beyond SMILES & SELFIES—0
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation—0
Sound Abstraction and Decomposition of Probabilistic Programs—0
Spreadsheet Probabilistic Programming—0
Static Analysis for Probabilistic Programs—0
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process—0
Stochastically Differentiable Probabilistic Programs—0
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
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
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