Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models
Fuente:
arXiv
Saved in:
| Main Authors: | Kündig, Pascal, Sigrist, Fabio |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
by: Kündig, Pascal, et al.
Published: (2025)
by: Kündig, Pascal, et al.
Published: (2025)
Gaussian Process Boosting
by: Sigrist, Fabio
Published: (2020)
by: Sigrist, Fabio
Published: (2020)
Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
by: Nava, Andrea, et al.
Published: (2026)
by: Nava, Andrea, et al.
Published: (2026)
A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios
by: Kündig, Pascal, et al.
Published: (2024)
by: Kündig, Pascal, et al.
Published: (2024)
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
by: Gyger, Tim, et al.
Published: (2024)
by: Gyger, Tim, et al.
Published: (2024)
Riemannian Laplace Approximation with the Fisher Metric
by: Yu, Hanlin, et al.
Published: (2023)
by: Yu, Hanlin, et al.
Published: (2023)
Review of Recent Advances in Gaussian Process Regression Methods
by: Lyu, Chenyi, et al.
Published: (2024)
by: Lyu, Chenyi, et al.
Published: (2024)
A Neural-Mean Vecchia Gaussian Process for Unified Argo Modeling
by: Liu, Nian, et al.
Published: (2025)
by: Liu, Nian, et al.
Published: (2025)
Diffusion-aware Censored Gaussian Processes for Demand Modelling
by: Rodrigues, Filipe
Published: (2025)
by: Rodrigues, Filipe
Published: (2025)
Aggregation Models with Optimal Weights for Distributed Gaussian Processes
by: Chen, Haoyuan, et al.
Published: (2024)
by: Chen, Haoyuan, et al.
Published: (2024)
Modular Jump Gaussian Processes
by: Flowers, Anna R., et al.
Published: (2025)
by: Flowers, Anna R., et al.
Published: (2025)
A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model
by: Chen, Yehu, et al.
Published: (2025)
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)
by: Islam, Md Shafiqul, et al.
Published: (2026)
Conditioning Gaussian Processes on Almost Anything
by: Moss, Henry, et al.
Published: (2026)
by: Moss, Henry, et al.
Published: (2026)
Self-Supervised Learning with Gaussian Processes
by: Duan, Yunshan, et al.
Published: (2025)
by: Duan, Yunshan, et al.
Published: (2025)
Improving Spatio-temporal Gaussian Process Modeling with Vecchia Approximation: A Low-Cost Sensor-Driven Approach to Urban Environmental Monitoring
by: Idir, Yacine Mohamed, et al.
Published: (2025)
by: Idir, Yacine Mohamed, et al.
Published: (2025)
A Bayesian Take on Gaussian Process Networks
by: Giudice, Enrico, et al.
Published: (2023)
by: Giudice, Enrico, et al.
Published: (2023)
Bayesian Causal Inference with Gaussian Process Networks
by: Giudice, Enrico, et al.
Published: (2024)
by: Giudice, Enrico, et al.
Published: (2024)
New Bounds for Sparse Variational Gaussian Processes
by: Titsias, Michalis K.
Published: (2025)
by: Titsias, Michalis K.
Published: (2025)
Automatic Laplace Collapsed Sampling: Scalable Marginalisation of Latent Parameters via Automatic Differentiation
by: Lovick, Toby, et al.
Published: (2026)
by: Lovick, Toby, et al.
Published: (2026)
Low-rank variational Bayes correction to the Laplace method
by: van Niekerk, Janet, et al.
Published: (2021)
by: van Niekerk, Janet, et al.
Published: (2021)
A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics
by: Gosnell, Arron, et al.
Published: (2024)
by: Gosnell, Arron, et al.
Published: (2024)
Latent Factor Point Processes for Patient Representation in Electronic Health Records
by: Knight, Parker, et al.
Published: (2025)
by: Knight, Parker, et al.
Published: (2025)
Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
by: Luo, Hengrui, et al.
Published: (2026)
by: Luo, Hengrui, et al.
Published: (2026)
Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes
by: Gu, Minghao, et al.
Published: (2026)
by: Gu, Minghao, et al.
Published: (2026)
Vecchia approximated Bayesian heteroskedastic Gaussian processes
by: Patil, Parul V., et al.
Published: (2025)
by: Patil, Parul V., et al.
Published: (2025)
Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
by: Pion, Aurélien, et al.
Published: (2026)
by: Pion, Aurélien, et al.
Published: (2026)
Robust Inference Methods for Latent Group Panel Models under Possible Group Non-Separation
by: Akgun, Oguzhan, et al.
Published: (2025)
by: Akgun, Oguzhan, et al.
Published: (2025)
Robust and Conjugate Spatio-Temporal Gaussian Processes
by: Laplante, William, et al.
Published: (2025)
by: Laplante, William, et al.
Published: (2025)
Towards Identifiable Latent Additive Noise Models
by: Liu, Yuhang, et al.
Published: (2024)
by: Liu, Yuhang, et al.
Published: (2024)
Proximal Approximate Inference in State-Space Models
by: Abdulsamad, Hany, et al.
Published: (2025)
by: Abdulsamad, Hany, et al.
Published: (2025)
Identifiable Deep Latent Variable Models for MNAR Data
by: Xie, Huiming, et al.
Published: (2026)
by: Xie, Huiming, et al.
Published: (2026)
Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models
by: Ma, Chenchen, et al.
Published: (2021)
by: Ma, Chenchen, et al.
Published: (2021)
Predicting Electricity Consumption with Random Walks on Gaussian Processes
by: Hashimoto-Cullen, Chloé, et al.
Published: (2024)
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)
by: Tan, Yu, et al.
Published: (2025)
Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Input
by: Kang, Lulu, et al.
Published: (2025)
by: Kang, Lulu, et al.
Published: (2025)
Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
by: Zhou, Zeyu, et al.
Published: (2023)
by: Zhou, Zeyu, et al.
Published: (2023)
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes
by: Feng, Brandon R., et al.
Published: (2024)
by: Feng, Brandon R., et al.
Published: (2024)
Causal Effect Identification in LiNGAM Models with Latent Confounders
by: Tramontano, Daniele, et al.
Published: (2024)
by: Tramontano, Daniele, et al.
Published: (2024)
Knowledge Distillation of Uncertainty using Deep Latent Factor Model
by: Park, Sehyun, et al.
Published: (2025)
by: Park, Sehyun, et al.
Published: (2025)
Similar Items
-
Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
by: Kündig, Pascal, et al.
Published: (2025) -
Gaussian Process Boosting
by: Sigrist, Fabio
Published: (2020) -
Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
by: Nava, Andrea, et al.
Published: (2026) -
A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios
by: Kündig, Pascal, et al.
Published: (2024) -
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
by: Gyger, Tim, et al.
Published: (2024)