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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
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages—0
Measuring the reliability of MCMC inference with bidirectional Monte Carlo—0
The Physics of Text: Ontological Realism in Information Extraction—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
A Step from Probabilistic Programming to Cognitive Architectures—0
Dataflow Matrix Machines as a Generalization of Recurrent Neural NetworksCode0
Composing inference algorithms as program transformations—0
Automatic Differentiation Variational InferenceCode1
A theory of contemplation—0
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)—0
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