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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.

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Papers

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Showing 141–150 of 273 papers

TitleStatusHype
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyroCode0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic ProgrammingCode0
Bayesian causal inference via probabilistic program synthesis—0
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
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