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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 181–190 of 273 papers

TitleStatusHype
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
Large Language Bayes—0
Lazy Factored Inference for Functional Probabilistic Programming—0
LazyPPL: laziness and types in non-parametric probabilistic programs—0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Learning Probabilistic Programs—0
Learning Probabilistic Programs Using Backpropagation—0
Linear Models of Computation and Program Learning—0
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