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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 151–160 of 273 papers

TitleStatusHype
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows—0
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs—0
Static Analysis for Probabilistic Programs—0
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
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
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