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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 51–100 of 273 papers

TitleStatusHype
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
Bayesian Policy Search for Stochastic Domains—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—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
BayesDB: A probabilistic programming system for querying the probable implications of data—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
A Step from Probabilistic Programming to Cognitive Architectures—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
Dimensionality Reduction as Probabilistic Inference—0
A Compilation Target for Probabilistic Programming Languages—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
A theory of contemplation—0
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach—0
Automatic Variational Inference in Stan—0
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
Anytime Exact Belief Propagation—0
From Probabilistic Programming to Complexity-based Programming—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
High Five: Improving Gesture Recognition by Embracing Uncertainty—0
Deep Probabilistic Programming—0
Automatic Inference for Inverting Software Simulators via 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
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs—0
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
Deployable probabilistic programming—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Detecting and Quantifying Malicious Activity with Simulation-based Inference—0
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)—0
Detecting Parameter Symmetries in Probabilistic Models—0
BayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling—0
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
Declarative Statistical Modeling with Datalog—0
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms—0
Doubly Bayesian Optimization—0
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains—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
Bayesian causal inference via probabilistic program synthesis—0
Efficient Search-Based Weighted Model Integration—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
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