Scalable Bayesian modelling for smoothing disease risks in large spatial data sets using INLA

Improving Bayesian Local Spatial Models in Large Datasets

(2020) Amanda Lenzi et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Global Consensus Monte Carlo

(2020) Lewis J. Rendell et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

A General Framework for Vecchia Approximations of Gaussian Processes

(2020) Matthias Katzfuss et al.   STATISTICAL SCIENCE

Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets

(2018) Rajarshi Guhaniyogi et al.   TECHNOMETRICS

Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018

(2018) J. Ferlay et al.   EUROPEAN JOURNAL OF CANCER

Stan: A Probabilistic Programming Language

(2017) Bob Carpenter et al.   Journal of Statistical Software

A Multi-Resolution Approximation for Massive Spatial Datasets

(2017) Matthias Katzfuss   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results

(2017) T. Goicoa et al.   STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

Comparing consensus Monte Carlo strategies for distributed Bayesian computation

(2017) Steven L. Scott   Brazilian Journal of Probability and Statistics

Bayesian Computing with INLA: A Review

(2017) Håvard Rue et al.   Annual Review of Statistics and Its Application

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

(2016) Abhirup Datta et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Parallel inference for massive distributed spatial data using low-rank models

(2016) Matthias Katzfuss et al.   STATISTICS AND COMPUTING

A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets

(2015) Douglas Nychka et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Local Gaussian Process Approximation for Large Computer Experiments

(2015) Robert B. Gramacy et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Parallelizing Gaussian Process Calculations inR

(2015) Christopher J. Paciorek et al.   Journal of Statistical Software

Understanding predictive information criteria for Bayesian models

(2013) Andrew Gelman et al.   STATISTICS AND COMPUTING

Dimension reduction and alleviation of confounding for spatial generalized linear mixed models

(2012) John Hughes et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

(2011) Finn Lindgren et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love

(2010) James S. Hodges et al.   AMERICAN STATISTICIAN

Fixed rank kriging for very large spatial data sets

(2010) Noel Cressie et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

On Gaussian Markov random fields and Bayesian disease mapping

(2010) Ying C MacNab   STATISTICAL METHODS IN MEDICAL RESEARCH

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

(2009) Håvard Rue et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Gaussian predictive process models for large spatial data sets

(2008) Sudipto Banerjee et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY