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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 211–220 of 273 papers

TitleStatusHype
Importance Sampled Stochastic Optimization for Variational Inference—0
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric BayesCode0
Deep Probabilistic Programming—0
A Convenient Category for Higher-Order Probability Theory—0
Adversarial Message Passing For Graphical Models—0
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming—0
Summary - TerpreT: A Probabilistic Programming Language for Program Induction—0
A Probabilistic Programming Approach To Probabilistic Data Analysis—0
Better call Saul: Flexible Programming for Learning and Inference in NLPCode0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
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