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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 101–125 of 273 papers

TitleStatusHype
EinSteinVI: General and Integrated Stein Variational Inference—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
Bayesian Policy Search for Stochastic Domains—0
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs—0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs—0
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
From Probabilistic Programming to Complexity-based Programming—0
Decision-Making with Complex Data Structures using Probabilistic Programming—0
Data Petri Nets meet Probabilistic Programming (Extended version)—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming—0
A Step from Probabilistic Programming to Cognitive Architectures—0
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
High Five: Improving Gesture Recognition by Embracing Uncertainty—0
Hijacking Malaria Simulators with Probabilistic Programming—0
Adversarial Message Passing For Graphical Models—0
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