Bayesian Structure

Bayesian structure learning focuses on inferring the probabilistic relationships between variables, often represented as directed acyclic graphs (DAGs), from data. Current research emphasizes developing efficient algorithms, such as those employing generative flow networks or variational autoencoders, to handle the computational challenges of exploring the vast space of possible graph structures, particularly for high-dimensional data and complex models like nonseparable Hamiltonians. These advancements improve the accuracy and scalability of Bayesian network inference, with applications ranging from gene regulatory network analysis to robust model updating in engineering and uncertainty quantification in machine learning.

Papers

September 27, 2024

Toward Universal and Interpretable World Models for Open-ended Learning Agents
Lancelot Da Costa
World Model Bayesian Network Open Ended Generative World Model Bayesian Structure

July 11, 2024

Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning
Dae-Won Kim, Song Ko, Bo-Yeong Kang
Estimation Task Bayesian Learning Evolutionary Optimization Single Point Mutation Mutation Operator Bayesian Structure Distribution Algorithm

June 7, 2024

Bayesian Structural Model Updating with Multimodal Variational Autoencoder
Tatsuya Itoi, Kazuho Amishiki, Sangwon Lee, Taro Yaoyama
Unimodal Encoders Bayesian Structure Multimodal Variational Bayesian Update Strain Estimation

January 23, 2024

Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling
Nicholas Galioto, Harsh Sharma, Boris Kramer, Alex Arkady Gorodetsky
Deep Learning Reduced Order Multiplicative Noise Bayesian Structure

October 19, 2023

Be Bayesian by Attachments to Catch More Uncertainty
Shiyu Shen, Bin Pan, Tianyang Shi, Tao Li, Zhenwei Shi
Uncertainty Estimation Bayesian Neural Network Document Image Bayesian Structure State of the Art Uncertainty

September 4, 2023

Differentiable Bayesian Structure Learning with Acyclicity Assurance
Quang-Duy Tran, Phuoc Nguyen, Bao Duong, Thin Nguyen
Score Based Acyclic Graph Bayesian Structure

June 20, 2023

A Bayesian Take on Gaussian Process Networks
Enrico Giudice, Jack Kuipers, Giusi Moffa
Gaussian Process Bayesian Perspective Probabilistic Graphical Model Bayesian Structure

March 8, 2023

Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level
Chase Yakaboski, Eugene Santos
Strong Generalization Bayesian Network Probabilistic Graphical Model Regulatory Network Bayesian Structure Instantiation Based

February 23, 2023

Bayesian Structure Scores for Probabilistic Circuits
Yang Yang, Gennaro Gala, Robert Peharz
Expectation Maximization Probabilistic Graphical Model Structure Learning Probabilistic Circuit Bayesian Structure

February 8, 2023

DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
Lazar Atanackovic, Alexander Tong, Bo Wang, Leo J. Lee, Yoshua Bengio, Jason Hartford
Bayesian Inference Generative Flow Network Energy Based GFlowNets Regulatory Network Feedback Loop Bayesian Structure

April 29, 2022

Tractable Uncertainty for Structure Learning
Benjie Wang, Matthew Wicker, Marta Kwiatkowska
Structure Learning Bayesian Structure Posterior Uncertainty

February 28, 2022

Bayesian Structure Learning with Generative Flow Networks
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio
Generative Modeling Variational Inference Bayesian Network Generative Flow Network Bayesian Structure

January 28, 2022

BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs
Federico Castelletti, Alessandro Mascaro
Causal Discovery Causal Relationship Causal Effect Estimation Causal Learning Causal Structure Learning R Package Bayesian Structure Bayesian Causal Discovery Gaussian DAG