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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 101–150 of 273 papers

TitleStatusHype
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming—0
Measuring the reliability of MCMC inference with bidirectional Monte Carlo—0
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism—0
Nested Reasoning About Autonomous Agents Using Probabilistic Programs—0
Modelling contextuality by probabilistic programs with hypergraph semantics—0
Multi-Model Probabilistic Programming—0
Nesting Probabilistic Programs—0
Neural Distribution Learning for generalized time-to-event prediction—0
Neural Probabilistic Logic Programming in Discrete-Continuous Domains—0
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels—0
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference—0
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs—0
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support—0
Particle Gibbs with Ancestor Sampling for Probabilistic Programs—0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming—0
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty—0
Picture: A Probabilistic Programming Language for Scene Perception—0
Pixyz: a Python library for developing deep generative models—0
Practical optimal experiment design with probabilistic programs—0
Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features—0
Probabilistic Planning by Probabilistic Programming—0
Probabilistic Programming Bots in Intuitive Physics Game Play—0
Probabilistic Programming with Gaussian Process Memoization—0
Probabilistic Programming with Programmable Variational Inference—0
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
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
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