Cover Image

Scalable Bayesian Inference for Generalized Linear Mixed Models via Stochastic Gradient MCMC

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Berchuck, Samuel I., Baek, Youngsoo, Medeiros, Felipe A., Agazzi, Andrea
Format:	Preprint
Published:	2024
Subjects:	Computation Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Safe, Scalable, and Accurate Bayes Posterior Sampling for Large-Data Generalized Linear Mixed Models
by: Baek, Youngsoo, et al.
Published: (2026)

Scalable Subset Selection in Linear Mixed Models
by: Thompson, Ryan, et al.
Published: (2025)

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
by: Saxena, Manan, et al.
Published: (2024)

Generative Bayesian Inference with GANs
by: Wang, Yuexi, et al.
Published: (2022)

Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis
by: Zhu, Haonan, et al.
Published: (2025)

Bayesian Federated Inference for Survival Models
by: Pazira, Hassan, et al.
Published: (2024)

How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation
by: Binette, Olivier, et al.
Published: (2024)

Non-Vacuous Certification of Transport MCMC via Oscillation-Controlled Normalizing Flows
by: Hu, Jun
Published: (2026)

Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
by: Hassan, Conor, et al.
Published: (2024)

Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model
by: Chen, Yujie, et al.
Published: (2025)

Scalable Monte Carlo for Bayesian Learning
by: Fearnhead, Paul, et al.
Published: (2024)

Statistical Inference for Differentially Private Stochastic Gradient Descent
by: Xia, Xintao, et al.
Published: (2025)

Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching
by: Lu, Wenhui Sophia, et al.
Published: (2025)

Stochastic Gradient MCMC for Massive Geostatistical Data
by: Abba, Mohamed A., et al.
Published: (2024)

Gradient Boosted Mixed Models: Flexible Joint Estimation of Mean and Variance Components for Clustered Data
by: Prevett, Mitchell L., et al.
Published: (2025)

Variational Inference for Bayesian MIDAS Regression
by: Simeone, Luigi
Published: (2026)

Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
by: Polson, Nick, et al.
Published: (2026)

Hypergraph Generation via Structured Stochastic Diffusion
by: Nemeth, Christopher
Published: (2026)

Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
by: Mohammadi, Reza, et al.
Published: (2023)

An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
by: Liu, Qiao, et al.
Published: (2026)

Neural Generalized Mixed-Effects Models
by: Slavutsky, Yuli, et al.
Published: (2026)

Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
by: Sharrock, Louis
Published: (2026)

Pathwise Gradient Variance Reduction with Control Variates in Variational Inference
by: Ng, Kenyon, et al.
Published: (2024)

Incentive-Theoretic Bayesian Inference for Collaborative Science
by: Bates, Stephen, et al.
Published: (2023)

Block Empirical Likelihood Inference for Longitudinal Generalized Partially Linear Single-Index Models
by: Zhang, Tianni, et al.
Published: (2026)

Bayesian Nonlinear PDE Inference via Gaussian Process Collocation with Application to the Richards Equation
by: Yang, Yumo, et al.
Published: (2025)

Linear-cost unbiased posterior estimates for crossed effects and matrix factorization models via couplings
by: Ceriani, Paolo Maria, et al.
Published: (2024)

Distributed Learning of Generalized Linear Causal Networks
by: Ye, Qiaoling, et al.
Published: (2022)

Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
by: Kündig, Pascal, et al.
Published: (2025)

Generative Bayesian Filtering and Parameter Learning
by: Marcelli, Edoardo, et al.
Published: (2025)

Semi-Parametric Inference for Doubly Stochastic Spatial Point Processes: An Approximate Penalized Poisson Likelihood Approach
by: Cheng, Si, et al.
Published: (2023)

Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
by: Meng, Xianghao, et al.
Published: (2026)

Federated Variational Inference for Bayesian Mixture Models
by: Rao, Jackie, et al.
Published: (2025)

Debiased Bayesian Inference for High-dimensional Regression Models
by: Chen, Qihui, et al.
Published: (2025)

MCMC with Adaptive Principal-Component Transformation: Rotation-Invariant Universal Samplers for Bayesian Structural System Identification
by: Meng, Xianghao, et al.
Published: (2026)

Scalable Asynchronous Federated Modeling for Spatial Data
by: Shi, Jianwei, et al.
Published: (2025)

A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression
by: Xie, Dongyue, et al.
Published: (2026)

To MCMC or not to MCMC: Evaluating non-MCMC methods for Bayesian penalized regression
by: van Leeuwen, Florian D., et al.
Published: (2025)

Tight Generalization Error Bounds for Stochastic Gradient Descent in Non-convex Learning
by: Xiong, Wenjun, et al.
Published: (2025)

Generalizing Orthogonalization for Models with Non-Linearities
by: Rügamer, David, et al.
Published: (2024)