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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
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation—0
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
Proceedings 37th International Conference on Logic Programming (Technical Communications)—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
Pixyz: a Python library for developing deep generative models—0
Unifying incidence and prevalence under a time-varying general branching processCode0
Supervised Bayesian Specification Inference from Demonstrations—0
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data—0
Probabilistic Programming Bots in Intuitive Physics Game Play—0
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Survival prediction and risk estimation of Glioma patients using mRNA expressions—0
Recalibrating classifiers for interpretable abusive content detection—0
BayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling—0
Accelerating Metropolis-Hastings with Lightweight Inference CompilationCode0
Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy PredictionCode0
Simulation-based inference methods for particle physics—0
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program AnalysisCode0
Probabilistic Programs with Stochastic ConditioningCode0
Bayesian Policy Search for Stochastic Domains—0
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels—0
Transforming Probabilistic Programs for Model Checking—0
Uncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
Stochastically Differentiable Probabilistic Programs—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware—0
Automatic structured variational inferenceCode0
Joint Distributions for TensorFlow ProbabilityCode0
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach—0
Stochastic Probabilistic ProgramsCode0
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyroCode0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic ProgrammingCode0
Bayesian causal inference via probabilistic program synthesis—0
Parameter elimination in particle Gibbs samplingCode0
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support—0
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness—0
Functional Tensors for Probabilistic ProgrammingCode0
Amortized Rejection Sampling in Universal Probabilistic ProgrammingCode0
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic ProgrammingCode0
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