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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 151–175 of 273 papers

TitleStatusHype
Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming—0
Towards Verified Stochastic Variational Inference for Probabilistic ProgramsCode0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed samplingCode0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Deployable probabilistic programming—0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Automatic Reparameterisation of Probabilistic ProgramsCode0
Hijacking Malaria Simulators with Probabilistic Programming—0
Rotation Invariant Householder Parameterization for Bayesian PCACode0
Modular Deep Probabilistic ProgrammingCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Reversible Jump Probabilistic ProgrammingCode0
The Random Conditional Distribution for Higher-Order Probabilistic Inference—0
Applying Probabilistic Programming to Affective ComputingCode0
Efficient Search-Based Weighted Model Integration—0
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsCode0
Tensor Variable Elimination for Plated Factor Graphs—0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Doubly Bayesian Optimization—0
Bayesian Layers: A Module for Neural Network UncertaintyCode0
Nested Reasoning About Autonomous Agents Using Probabilistic Programs—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Joint Mapping and Calibration via Differentiable Sensor Fusion—0
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