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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 141–150 of 273 papers

TitleStatusHype
Parameter elimination in particle Gibbs samplingCode0
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support—0
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness—0
Functional Tensors for Probabilistic ProgrammingCode0
Amortized Rejection Sampling in Universal Probabilistic ProgrammingCode0
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic ProgrammingCode0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows—0
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs—0
Static Analysis for Probabilistic Programs—0
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