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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 151–200 of 273 papers

TitleStatusHype
Venture: a higher-order probabilistic programming platform with programmable inference—0
Weighted Programming—0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
WOLFE: An NLP-friendly Declarative Machine Learning Stack—0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Effect Handling for Composable Program Transformations in Edward2—0
Efficient Incremental Belief Updates Using Weighted Virtual Observations—0
Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows—0
Efficient Search-Based Weighted Model Integration—0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs—0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs—0
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
From Probabilistic Programming to Complexity-based Programming—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
High Five: Improving Gesture Recognition by Embracing Uncertainty—0
Hijacking Malaria Simulators with Probabilistic Programming—0
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic—0
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data—0
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
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