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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 176–200 of 273 papers

TitleStatusHype
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis—0
Importance Sampled Stochastic Optimization for Variational Inference—0
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators—0
Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework—0
Inference Over Programs That Make Predictions—0
Inference Plans for Hybrid Particle Filtering—0
Inferring Capabilities from Task Performance with Bayesian Triangulation—0
Joint Mapping and Calibration via Differentiable Sensor Fusion—0
Large Language Bayes—0
Lazy Factored Inference for Functional Probabilistic Programming—0
LazyPPL: laziness and types in non-parametric probabilistic programs—0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Learning Probabilistic Programs—0
Learning Probabilistic Programs Using Backpropagation—0
Linear Models of Computation and Program Learning—0
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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