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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 251–273 of 273 papers

TitleStatusHype
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs—0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—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
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
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
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic—0
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data—0
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis—0
Importance Sampled Stochastic Optimization for Variational Inference—0
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators—0
Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework—0
Inference Over Programs That Make Predictions—0
Inference Plans for Hybrid Particle Filtering—0
Inferring Capabilities from Task Performance with Bayesian Triangulation—0
Joint Mapping and Calibration via Differentiable Sensor Fusion—0
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