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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 201–250 of 273 papers

TitleStatusHype
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 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
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming—0
Composing inference algorithms as program transformations—0
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations—0
Consistent Kernel Mean Estimation for Functions of Random Variables—0
Data-driven Sequential Monte Carlo in Probabilistic Programming—0
Data Petri Nets meet Probabilistic Programming (Extended version)—0
Decision-Making with Complex Data Structures using Probabilistic Programming—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains—0
Declarative Statistical Modeling with Datalog—0
Deep Probabilistic Programming—0
Deep Probabilistic Programming Languages: A Qualitative Study—0
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach—0
Deployable probabilistic programming—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Detecting and Quantifying Malicious Activity with Simulation-based Inference—0
Detecting Parameter Symmetries in Probabilistic Models—0
Dimensionality Reduction as Probabilistic Inference—0
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms—0
Doubly Bayesian Optimization—0
Effect Handling for Composable Program Transformations in Edward2—0
Efficient Incremental Belief Updates Using Weighted Virtual Observations—0
Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows—0
Efficient Search-Based Weighted Model Integration—0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
EinSteinVI: General and Integrated Stein Variational Inference—0
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