Cover Image

A Bayesian Take on Gaussian Process Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Giudice, Enrico, Kuipers, Jack, Moffa, Giusi
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

A Latent Causal Inference Framework for Ordinal Variables
by: Scauda, Martina, et al.
Published: (2025)

Exact discovery is polynomial for certain sparse causal Bayesian networks
by: Rios, Felix L., et al.
Published: (2024)

A new way to evaluate G-Wishart normalising constants via Fourier analysis
by: Wong, Ching, et al.
Published: (2024)

Scaling Up Bayesian DAG Sampling
by: Nikzad, Daniele, et al.
Published: (2025)

The covariance of causal effect estimators for binary v-structures
by: Kuipers, Jack, et al.
Published: (2025)

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

Exact Bayesian Gaussian Cox Processes Using Random Integral
by: Tang, Bingjing, et al.
Published: (2024)

Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes
by: Feng, Brandon R., et al.
Published: (2024)

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

Modular Jump Gaussian Processes
by: Flowers, Anna R., et al.
Published: (2025)

Conditioning Gaussian Processes on Almost Anything
by: Moss, Henry, et al.
Published: (2026)

Self-Supervised Learning with Gaussian Processes
by: Duan, Yunshan, et al.
Published: (2025)

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
by: James, Zachary, et al.
Published: (2025)

Gaussian Process Boosting
by: Sigrist, Fabio
Published: (2020)

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

On a conjecture of Roverato regarding G-Wishart normalising constants
by: Wong, Ching, et al.
Published: (2025)

New Bounds for Sparse Variational Gaussian Processes
by: Titsias, Michalis K.
Published: (2025)

A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model
by: Chen, Yehu, et al.
Published: (2025)

The Kernel Manifold: A Geometric Approach to Gaussian Process Model Selection
by: Islam, Md Shafiqul, et al.
Published: (2026)

Diffusion-aware Censored Gaussian Processes for Demand Modelling
by: Rodrigues, Filipe
Published: (2025)

Aggregation Models with Optimal Weights for Distributed Gaussian Processes
by: Chen, Haoyuan, et al.
Published: (2024)

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

Design-marginal calibration of Gaussian process predictive distributions: Bayesian and conformal approaches
by: Pion, Aurélien, et al.
Published: (2025)

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

Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes
by: Gu, Minghao, et al.
Published: (2026)

Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models
by: Kündig, Pascal, et al.
Published: (2023)

A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics
by: Gosnell, Arron, et al.
Published: (2024)

Robust and Conjugate Spatio-Temporal Gaussian Processes
by: Laplante, William, et al.
Published: (2025)

Predicting Electricity Consumption with Random Walks on Gaussian Processes
by: Hashimoto-Cullen, Chloé, et al.
Published: (2024)

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
by: Tan, Yu, et al.
Published: (2025)

Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization
by: Petit, Sébastien, et al.
Published: (2022)

Gaussian credible intervals in Bayesian nonparametric estimation of the unseen
by: Contardi, Claudia, et al.
Published: (2025)

Hard and Soft EM in Bayesian Network Learning from Incomplete Data
by: Ruggieri, Andrea, et al.
Published: (2020)

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

Integrative Learning of Dynamically Evolving Multiplex Graphs and Nodal Attributes Using Neural Network Gaussian Processes with an Application to Dynamic Terrorism Graphs
by: Rodriguez-Acosta, Jose, et al.
Published: (2026)

Fast and robust Bayesian Inference using Gaussian Processes with GPry
by: Gammal, Jonas El, et al.
Published: (2022)

Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks
by: Sheinkman, Alisa, et al.
Published: (2025)

A Bayesian Framework for Post-disruption Travel Time Prediction in Metro Networks
by: Nazemi, Shayan, et al.
Published: (2026)

Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting
by: Iong, Daniel, et al.
Published: (2024)