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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 151–200 of 273 papers

TitleStatusHype
Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming—0
Towards Verified Stochastic Variational Inference for Probabilistic ProgramsCode0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed samplingCode0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Deployable probabilistic programming—0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Automatic Reparameterisation of Probabilistic ProgramsCode0
Hijacking Malaria Simulators with Probabilistic Programming—0
Rotation Invariant Householder Parameterization for Bayesian PCACode0
Modular Deep Probabilistic ProgrammingCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Reversible Jump Probabilistic ProgrammingCode0
The Random Conditional Distribution for Higher-Order Probabilistic Inference—0
Applying Probabilistic Programming to Affective ComputingCode0
Efficient Search-Based Weighted Model Integration—0
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsCode0
Tensor Variable Elimination for Plated Factor Graphs—0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Doubly Bayesian Optimization—0
Bayesian Layers: A Module for Neural Network UncertaintyCode0
Nested Reasoning About Autonomous Agents Using Probabilistic Programs—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Joint Mapping and Calibration via Differentiable Sensor Fusion—0
Effect Handling for Composable Program Transformations in Edward2—0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
Simple, Distributed, and Accelerated Probabilistic ProgrammingCode0
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and DeterministicCode1
Pyro: Deep Universal Probabilistic ProgrammingCode0
Automated learning with a probabilistic programming language: Birch—0
Sinkhorn AutoEncodersCode0
Inference Over Programs That Make Predictions—0
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Neural Distribution Learning for generalized time-to-event prediction—0
An Introduction to Probabilistic ProgrammingCode0
Scenic: A Language for Scenario Specification and Scene GenerationCode1
A Fairness-aware Hybrid Recommender System—0
Machine Teaching of Active Sequential LearnersCode0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
Sound Abstraction and Decomposition of Probabilistic Programs—0
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms—0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Deep Probabilistic Programming Languages: A Qualitative Study—0
Hamiltonian Monte Carlo for Probabilistic Programs with DiscontinuitiesCode0
Nesting Probabilistic Programs—0
Modelling contextuality by probabilistic programs with hypergraph semantics—0
Probabilistic Planning by Probabilistic Programming—0
Bayesian Neural NetworksCode0
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