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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 241–250 of 273 papers

TitleStatusHype
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints—0
Probabilistic Programming with Gaussian Process Memoization—0
BayesDB: A probabilistic programming system for querying the probable implications of data—0
Linear Models of Computation and Program Learning—0
Data-driven Sequential Monte Carlo in Probabilistic Programming—0
Lazy Factored Inference for Functional Probabilistic Programming—0
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching—0
RELLY: Inferring Hypernym Relationships Between Relational Phrases—0
A New Approach to Probabilistic Programming Inference—0
Automatic Variational Inference in Stan—0
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