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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 126–150 of 273 papers

TitleStatusHype
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
Planning as Inference in Epidemiological ModelsCode1
Stochastically Differentiable Probabilistic Programs—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
DynamicPPL: Stan-like Speed for Dynamic Probabilistic ModelsCode1
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware—0
Automatic structured variational inferenceCode0
Joint Distributions for TensorFlow ProbabilityCode0
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach—0
Stochastic Probabilistic ProgramsCode0
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
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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