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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 231–240 of 273 papers

TitleStatusHype
Data Petri Nets meet Probabilistic Programming (Extended version)—0
Decision-Making with Complex Data Structures using Probabilistic Programming—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains—0
Declarative Statistical Modeling with Datalog—0
Deep Probabilistic Programming—0
Deep Probabilistic Programming Languages: A Qualitative Study—0
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach—0
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