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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

Showing 201250 of 273 papers

TitleStatusHype
Neural Distribution Learning for generalized time-to-event prediction0
Neural Probabilistic Logic Programming in Discrete-Continuous Domains0
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels0
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference0
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation0
Particle Gibbs with Ancestor Sampling for Probabilistic Programs0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming0
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty0
Picture: A Probabilistic Programming Language for Scene Perception0
Pixyz: a Python library for developing deep generative models0
Practical optimal experiment design with probabilistic programs0
Diffusion models for probabilistic programmingCode0
Sinkhorn AutoEncodersCode0
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Machine Teaching of Active Sequential LearnersCode0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
Modular Deep Probabilistic ProgrammingCode0
Pyro: Deep Universal Probabilistic ProgrammingCode0
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic ProgrammingCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Differentiable Quantum Programming with Unbounded LoopsCode0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Towards Verified Stochastic Variational Inference for Probabilistic ProgramsCode0
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic ProgrammingCode0
Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy PredictionCode0
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program AnalysisCode0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
Functional Tensors for Probabilistic ProgrammingCode0
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyroCode0
Amortized Rejection Sampling in Universal Probabilistic ProgrammingCode0
Bayesian Layers: A Module for Neural Network UncertaintyCode0
An Introduction to Probabilistic ProgrammingCode0
Hamiltonian Monte Carlo for Probabilistic Programs with DiscontinuitiesCode0
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects ModelsCode0
Parameter elimination in particle Gibbs samplingCode0
Reversible Jump Probabilistic ProgrammingCode0
Borch: A Deep Universal Probabilistic Programming LanguageCode0
ZhuSuan: A Library for Bayesian Deep LearningCode0
Robust leave-one-out cross-validation for high-dimensional Bayesian modelsCode0
Ice Core Dating using Probabilistic ProgrammingCode0
Rotation Invariant Householder Parameterization for Bayesian PCACode0
Unifying incidence and prevalence under a time-varying general branching processCode0
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black BoxCode0
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