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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 221–230 of 273 papers

TitleStatusHype
Bayesian Policy Search for Stochastic Domains—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching—0
Compartmental Models for COVID-19 and Control via Policy Interventions—0
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming—0
Composing inference algorithms as program transformations—0
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations—0
Consistent Kernel Mean Estimation for Functions of Random Variables—0
Data-driven Sequential Monte Carlo in Probabilistic Programming—0
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