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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 201–225 of 273 papers

TitleStatusHype
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
A Step from Probabilistic Programming to Cognitive Architectures—0
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
Augur: Data-Parallel Probabilistic Modeling—0
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions—0
Automated learning with a probabilistic programming language: Birch—0
Automated Variational Inference in Probabilistic Programming—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Automatic Inference for Inverting Software Simulators via Probabilistic Programming—0
Automatic Variational Inference in Stan—0
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)—0
BayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling—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
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
Bayesian Policy Search for Stochastic Domains—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching—0
Compartmental Models for COVID-19 and Control via Policy Interventions—0
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