Cover Image

Scalable Gaussian Process Inference with Stan

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hoffmann, Till, Onnela, Jukka-Pekka
Format:	Preprint
Published:	2023
Subjects:	Methodology Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bayesian Inference for Sexual Contact Networks Using Longitudinal Survey Data
by: Hoffmann, Till, et al.
Published: (2025)

Approximate Inference for Longitudinal Mechanistic HIV Contact Networks
by: Smiley, Octavious, et al.
Published: (2024)

Unifying Summary Statistic Selection for Approximate Bayesian Computation
by: Hoffmann, Till, et al.
Published: (2022)

Network Layout Algorithm with Covariate Smoothing
by: Smiley, Octavious, et al.
Published: (2024)

Temporal Configuration Model: Statistical Inference and Spreading Processes
by: Le, Thien-Minh, et al.
Published: (2024)

Information is localized in growing network models
by: Hoffmann, Till, et al.
Published: (2025)

A Generalized Estimating Equation Approach to Network Regression
by: Ghosh, Riddhi Pratim, et al.
Published: (2023)

Covariate Selection for Joint Latent Space Modeling of Sparse Network Data
by: Crenshaw, Emma G, et al.
Published: (2026)

Identification and Estimation of Heterogeneous Interference Effects under Unknown Network
by: Zhang, Yuhua, et al.
Published: (2025)

Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection
by: Matabuena, Marcos, et al.
Published: (2024)

Community Detection through Recursive Partitioning in Bayesian Framework
by: Zhang, Yuhua, et al.
Published: (2025)

Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
by: Wang, Maxwell H, et al.
Published: (2025)

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies
by: Fowler, Charlotte, et al.
Published: (2022)

Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs
by: Matabuena, Marcos, et al.
Published: (2024)

Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
by: Matabuena, Marcos, et al.
Published: (2024)

Tutorial on Bayesian Functional Regression Using Stan
by: Jiang, Ziren, et al.
Published: (2025)

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
by: Matabuena, Marcos, et al.
Published: (2025)

Causal estimands and identification of time-varying effects in non-stationary time series from N-of-1 mobile device data
by: Cai, Xiaoxuan, et al.
Published: (2024)

Improvements on Scalable Stochastic Bayesian Inference Methods for Multivariate Hawkes Process
by: Jiang, Alex Ziyu, et al.
Published: (2023)

StanBKT: Rethinking Parameter Estimation in Bayesian Knowledge Tracing
by: Pradhan, Siddhartha, et al.
Published: (2026)

Missing data in non-stationary multivariate time series from digital studies in Psychiatry
by: Cai, Xiaoxuan, et al.
Published: (2025)

Estimating Default Probability and Correlation using Stan
by: Pinera-Esquivel, Jesus A.
Published: (2024)

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

Fast and Scalable Inference for Spatial Extreme Value Models
by: Chen, Meixi, et al.
Published: (2021)

Stratified distance space improves the efficiency of sequential samplers for approximate Bayesian computation
by: Pesonen, Henri, et al.
Published: (2023)

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

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

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

Exchangeable Gaussian Processes with application to epidemics
by: Bouranis, Lampros, et al.
Published: (2025)

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking
by: Pan, Soumyakanti, et al.
Published: (2024)

Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data
by: Peruzzi, Michele, et al.
Published: (2021)

A Neural-Mean Vecchia Gaussian Process for Unified Argo Modeling
by: Liu, Nian, et al.
Published: (2025)

Scalable Fitting Methods for Multivariate Gaussian Additive Models with Covariate-dependent Covariance Matrices
by: Gioia, Vincenzo, et al.
Published: (2025)

Inference for Diffusion Processes via Controlled Sequential Monte Carlo and Splitting Schemes
by: Huang, Shu, et al.
Published: (2025)

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

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

Inference for Delay Differential Equations Using Manifold-Constrained Gaussian Processes
by: Zhao, Yuxuan, et al.
Published: (2024)

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

Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking
by: Wakayama, Tomoya, et al.
Published: (2024)

Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes
by: Zhu, Yichen, et al.
Published: (2022)