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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 26–50 of 273 papers

TitleStatusHype
Scenic: A Language for Scenario Specification and Data GenerationCode1
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and DeterministicCode1
Nonparametric Involutive Markov Chain Monte CarloCode1
Automatic Differentiation Variational InferenceCode1
PPL Bench: Evaluation Framework For Probabilistic Programming LanguagesCode1
Conditional independence by typingCode1
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
A Step from Probabilistic Programming to Cognitive Architectures—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
A Probabilistic Programming Approach To Probabilistic Data Analysis—0
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs—0
A Fairness-aware Hybrid Recommender System—0
A Convenient Category for Higher-Order Probability Theory—0
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support—0
BayesDB: A probabilistic programming system for querying the probable implications of data—0
Bayesian causal inference via probabilistic program synthesis—0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
A Compilation Target for Probabilistic Programming Languages—0
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