Cover Image

Bayesian Bridge Gaussian Process Regression

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Minshen, Lan, Shiwei, Kang, Lulu
Format:	Preprint
Published:	2025
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Input
by: Kang, Lulu, et al.
Published: (2025)

Energetic Variational Gaussian Process Regression for Computer Experiments
by: Kang, Lulu, et al.
Published: (2023)

Gaussian Process Assisted Active Learning of Physical Laws
by: Chen, Jiuhai, et al.
Published: (2019)

Bayesian Image-on-Image Regression via Deep Kernel Learning based Gaussian Processes
by: Ma, Guoxuan, et al.
Published: (2023)

Correlated Bayesian Additive Regression Trees with Gaussian Process for Regression Analysis of Dependent Data
by: a, Xuetao Lu, et al.
Published: (2023)

Bayesian Inference for Stationary Points in Gaussian Process Regression Models for Event-Related Potentials Analysis
by: Yu, Cheng-Han, et al.
Published: (2020)

Bayesian Inference of Spatially Varying Correlations via the Thresholded Correlation Gaussian Process
by: Li, Moyan, et al.
Published: (2024)

Spatiotemporal Besov Priors for Bayesian Inverse Problems
by: Lan, Shiwei, et al.
Published: (2023)

Bayesian Auxiliary Variable Model for Birth Records Data with Qualitative and Quantitative Responses
by: Kang, Xiaoning, et al.
Published: (2020)

Deep Q-Exponential Processes
by: Chang, Zhi, et al.
Published: (2024)

A Bayesian Generalized Bridge Regression Approach to Covariance Estimation in the Presence of Covariates
by: Zhao, Christina, et al.
Published: (2024)

Scalable Scalar-on-Image Cortical Surface Regression with a Relaxed-Thresholded Gaussian Process Prior
by: Menacher, Anna, et al.
Published: (2024)

Robust and Sparse Generalized Linear Models for High-Dimensional Data via Maximum Mean Discrepancy
by: Kang, Xiaoning, et al.
Published: (2026)

Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
by: Nava, Andrea, et al.
Published: (2026)

Bayesian Additive Regression Tree Copula Processes for Scalable Distributional Prediction
by: Wenkel, Jan Martin, et al.
Published: (2026)

Review of Recent Advances in Gaussian Process Regression Methods
by: Lyu, Chenyi, et al.
Published: (2024)

Local Bayesian Regression
by: Hjort, Nils Lid
Published: (2026)

Wrapped Gaussian Process Functional Regression Model for Batch Data on Riemannian Manifolds
by: Liu, Jinzhao, et al.
Published: (2024)

BSNMani: Bayesian Scalar-on-network Regression with Manifold Learning
by: Li, Yijun, et al.
Published: (2024)

A Bayesian Take on Gaussian Process Networks
by: Giudice, Enrico, et al.
Published: (2023)

Bayesian Causal Inference with Gaussian Process Networks
by: Giudice, Enrico, et al.
Published: (2024)

Quantile Forecast Matching with a Bayesian Quantile Gaussian Process Model
by: Wadsworth, Spencer, et al.
Published: (2025)

Bayesian Analysis of Sigmoidal Gaussian Cox Processes via Data Augmentation
by: Alie, Renaud, et al.
Published: (2022)

Going With the Flow: Normalizing Flows for Gaussian Process Regression under Hierarchical Shrinkage Priors
by: Knaus, Peter
Published: (2025)

Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
by: Luo, Hengrui, et al.
Published: (2026)

Bayesian Environment Invariant Regression
by: Zhang, Ruqian, et al.
Published: (2026)

The Bayesian Gaussian Process Latent Variable Model for Spatio-Temporal Stream Networks
by: Basson, Marno, et al.
Published: (2026)

Bayesian Variable Selection via Hierarchical Gaussian Process Model in Computer Experiments
by: Yao, Xiao, et al.
Published: (2024)

Compressed Bayesian Tensor Regression
by: Casarin, Roberto, et al.
Published: (2025)

Bayesian inference for group-level cortical surface image-on-scalar-regression with Gaussian process priors
by: Whiteman, Andrew S., et al.
Published: (2023)

Collaborative Analysis for Paired A/B Testing Experiments
by: Zhang, Qiong, et al.
Published: (2024)

A Maximin $Φ_{p}$-Efficient Design for Multivariate GLM
by: Li, Yiou, et al.
Published: (2020)

Robust Joint Modeling for Data with Continuous and Binary Responses
by: Wang, Yu, et al.
Published: (2026)

Covariate Balancing Based on Kernel Density Estimates for Controlled Experiments
by: Li, Yiou, et al.
Published: (2020)

Bayesian analysis of nonlinear structured latent factor models using a Gaussian Process Prior
by: Zhang, Yimang, et al.
Published: (2025)

Scalable Gaussian Process Regression Via Median Posterior Inference for Estimating Multi-Pollutant Mixture Health Effects
by: Sonabend, Aaron, et al.
Published: (2024)

Scalable Bayesian inference for heat kernel Gaussian processes on manifolds
by: He, Junhui, et al.
Published: (2024)

Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
by: Pion, Aurélien, et al.
Published: (2026)

Bayesian Partial Reduced-Rank Regression
by: Pintado, Maria F., et al.
Published: (2024)

Bayesian Structured Mediation Analysis With Unobserved Confounders
by: Xu, Yuliang, et al.
Published: (2024)