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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 91–100 of 273 papers

TitleStatusHype
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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