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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 191–200 of 273 papers

TitleStatusHype
Mapping probability word problems to executable representations—0
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming—0
Measuring the reliability of MCMC inference with bidirectional Monte Carlo—0
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism—0
Nested Reasoning About Autonomous Agents Using Probabilistic Programs—0
Modelling contextuality by probabilistic programs with hypergraph semantics—0
Multi-Model Probabilistic Programming—0
Nesting Probabilistic Programs—0
Neural Distribution Learning for generalized time-to-event prediction—0
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