Bayesian Model

Bayesian models offer a powerful framework for statistical inference by explicitly incorporating uncertainty into model parameters and predictions, enabling more robust and reliable results. Current research emphasizes applications across diverse fields, focusing on model architectures like Bayesian neural networks and Bayesian CART models, along with advanced inference techniques such as variational inference and Markov Chain Monte Carlo methods. This focus stems from the increasing need for uncertainty quantification in various applications, including medical imaging, financial modeling, and machine learning, leading to improved decision-making and more trustworthy predictions. The ability to incorporate prior knowledge and handle complex data structures makes Bayesian methods increasingly valuable for scientific discovery and practical problem-solving.

Papers

October 21, 2024

Hotel Booking Cancellation Prediction Using Applied Bayesian Models
Md Asifuzzaman Jishan, Vikas Singh, Ayan Kumar Ghosh, Md Shahabub Alam, Khan Raqib Mahmud, Bijan Paul
Bayesian Model Travel Mode Bayesian Regression Logistic Model Hotel Demand

October 20, 2024

On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization Guarantees
Yijie Zhang
Bayesian Inference Bayesian Approach PAC Bayesian Bayesian Deep Learning Generalization Guarantee Bayesian Model Probabilistic Neural Network Cold Posterior

September 17, 2024

Uncertainty Decomposition and Error Margin Detection of Homodyned-K Distribution in Quantitative Ultrasound
Dorsa Ameri, Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
Bayesian Neural Network Bayesian Model Quantitative Ultrasound K Distribution Error Simulation Uncertainty Decomposition

September 3, 2024

Bayesian CART models for aggregate claim modeling
Yaojun Zhang, Lanpeng Ji, Georgios Aivaliotis, Charles C. Taylor
Heavy Tailed Joint Modeling Bayesian Model Frequency Severity Modeling

July 22, 2024

Multimodal Input Aids a Bayesian Model of Phonetic Learning
Sophia Zhi, Roger P. Levy, Stephan C. Meylan
Audio Visual Multimodal Input Acoustic Feature Bayesian Model Phoneme Recognition

July 21, 2024

Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models
Jiaxiang Yi, Ji Cheng, Miguel A. Bessa
Hybrid Fusion High Fidelity Multi Fidelity Bayesian Model Fidelity Model Multi Fidelity Data

June 25, 2024

BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging
Zeinab Sherkatghanad, Moloud Abdar, Mohammadreza Bakhtyari, Pawel Plawiak, Vladimir Makarenkov
Deep Learning Model Medical Image Datasets Pre Trained Convolutional Neural Network Test Time Augmentation Bayesian Model Bayesian Model Averaging

May 2, 2024

Multivariate Bayesian Last Layer for Regression: Uncertainty Quantification and Disentanglement
Han Wang, Eiji Kawasaki, Guillaume Damblin, Geoffrey Daniel
Uncertainty Quantification Novel Regression Disentanglement Capability Bayesian Model Parameter Learning Bayesian Last Layer

March 28, 2024

On Uncertainty Quantification for Near-Bayes Optimal Algorithms
Ziyu Wang, Chris Holmes
Uncertainty Quantification Bayesian Network Posterior Inference Predictive Uncertainty Bayesian Model Bayes Optimal

March 22, 2024

Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models
John Fischer, Marko Orescanin, Justin Loomis, Patrick McClure
Application Proficiency Variational Inference Bayesian Deep Learning Bayesian Model Modern Deep Learning Mean Aggregation Bayesian Federated Learning

February 19, 2024

Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling
Elhadji C. Faye, Mame Diarra Fall, Nicolas Dobigeon
Super Resolution Bayesian Inference Bayesian Model Image Inversion Metropolis Adjusted Langevin Algorithm Data Dependent

February 10, 2024

Distilling Symbolic Priors for Concept Learning into Neural Networks
Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths
Neural Network Knowledge Distillation Inductive Bias Concept Learning Bayesian Model

December 1, 2023

Streaming Bayesian Modeling for predicting Fat-Tailed Customer Lifetime Value
Alexey V. Calabourdin, Konstantin A. Aksenov
Bayesian Model Thin Tail Customer Lifetime Value Lifetime Value

November 16, 2023

Bayes in the age of intelligent machines
Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy
Neural Network Bayesian Inference Human Cognition Speech Based Age Bayesian Model Bayes Rule Intelligent Machine

September 11, 2023

The fine print on tempered posteriors
Konstantinos Pitas, Julyan Arbel
Test Time Stochastic Process PAC Bayesian Bayesian Model Posterior Mode Contentious Term

August 28, 2023

Hierarchical Time Series Forecasting with Bayesian Modeling
Gal Elgavish
Time Series Data Bayesian Perspective Bayesian Model Univariate Time Series Forecasting Hierarchical Time Series Forecasting Forecast Reconciliation

June 16, 2023

Collapsed Inference for Bayesian Deep Learning
Zhe Zeng, Guy Van den Broeck
Bayesian Neural Network Bayesian Deep Learning Bayesian Model Inference Module

June 9, 2023

Bayesian Calibration of MEMS Accelerometers
Oliver Dürr, Po-Yu Fan, Zong-Xian Yin
Bayesian Approach Accelerometer Signal Bayesian Model Accelerometer Data Bayesian Calibration

May 12, 2023

Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition
Yuling Yao, Luiz Max Carvalho, Diego Mesquita, Yann McLatchie
Pooling Layer Posterior Distribution Bayesian Model Unlocking Insight First Order Superposition Stacking Ensemble Model Combination Bayesian Model Averaging

April 21, 2023

Machine Learning and the Future of Bayesian Computation
Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson
Machine Learning Bayesian Inference Future Reasoning Posterior Inference Posterior Approximation Bayesian Model Bayesian Computation