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1. Gaussian Markov random fields
: theory and applications
by Rue, Håvard and Held, Leonhard
2005, Monographs on statistics and applied probability (Series), ISBN 9781584884323, Volume 104, 263
Gaussian Markov Random Field (GMRF) models, most widely used in spatial statistics are presented in this, the first book on the subject that provides a unified... 
Gaussian Markov random fields
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2. Full Text Bayesian spatial modelling with R-INLA
by Lindgren, Finn and Rue, Håvard
Journal of Statistical Software, ISSN 1548-7660, 2015, Volume 63, Issue 19, pp. 1 - 25
The principles behind the interface to continuous domain spatial models in the R INLA software package for R are described. The integrated nested Laplace... 
Gaussian Markov random _elds | Stochastic partial differential equations | Bayesian inference | Laplace approximation | GAUSSIAN MODELS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | STATISTICS | stochastic partial differential equations | EQUATIONS | STATISTICS & PROBABILITY | Gaussian Markov random fields | DATA SETS
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3. Full Text An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach [with Discussion]
by Finn Lindgren and Håvard Rue and Johan Lindström and John T. Kent and Peter J. Diggle and J. B. Illian and D. P. Simpson and Tilmann Gneiting and Michael Scheuerer and R. Furrer and E. Furrer and D. Nychka and Paul Fearnhead and Peter Challenor and Yiannis Andrianakis and Gemma Stephenson and Jesper Møller and Xiangping Hu and Daniel Simpson and David Bolin and Patrick E. Brown and Michela Cameletti and Sara Martino and Daniel Cooley and Jennifer A. Hoeting and Rosa M. Crujeiras and Andrés Prieto and Marco A. R. Ferreira and Geir-Arne Fuglstad and Andrew Gelman and Peter Guttorp and Barnali Das and Ben Haaland and David J. Nott and John Haslett and Chaitanya Joshi and Vincent Garreta and Michael Höhle and L. Ippoliti and R. J. Martin and R. J. Bhansali and Venkata K. Jandhyala and Stergios B. Fotopoulos and Mikyoung Jun and Håvard Wahl Kongsgård and Giovanna Jona Lasinio and Alessio Pollice and Chihoon Lee and Wayne T. Lee and Cari G. Kaufman and Bo Li and Marc G. Genton and Georg Lindgren and K. V. Mardia and Jorge Mateu and Debashis Mondal and Werner G. Müller and Helmut Waldl and Alessandro Ottavi and Omiros Papaspiliopoulos and Emilio Porcu and Marc Saez and Alexandra M. Schmidt and Alfred Stein and Paul Switzer and Kamil Turkman and Christopher K. Wikle and Mevin B. Hooten
Journal of the Royal Statistical Society. Series B (Statistical Methodology), ISSN 1369-7412, 9/2011, Volume 73, Issue 4, pp. 423 - 498
Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the... 
Triangulation | Approximation | Covariance | Partial differential equations | Mathematical functions | Markov models | Spatial models | Covariance matrices | Parametric models | Modeling | Latent Gaussian models | Stochastic partial differential equations | Covariance functions | Gaussian fields | Approximate Bayesian inference | Gaussian Markov random fields | Sparse matrices | Gaussian markov random fields | Approximate bayesian inference | Latent gaussian models | STATISTICS | RECONSTRUCTION | STATISTICS & PROBABILITY | SIMULATION | PREDICTION | MODELS | FRAMEWORK | SPLINES | BAYESIAN-INFERENCE | LEVY | Markov processes | Usage | Models | Algorithms | Analysis | Parametrization | Computation | Mathematical analysis | Images | Mathematical models | Gaussian | Stochasticity | Mathematics | Naturvetenskap | Natural Sciences | Matematik | Sannolikhetsteori och statistik | Probability Theory and Statistics
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4. Full Text Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations
by Håvard Rue and Sara Martino and Nicolas Chopin
Journal of the Royal Statistical Society. Series B (Statistical Methodology), ISSN 1369-7412, 4/2009, Volume 71, Issue 2, pp. 319 - 392
Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized)... 
Statistical discrepancies | Gaussian distributions | Approximation | Inference | Markov chains | Non Gaussianity | Stochastic models | Bayesian inference | Modeling | Spatial models | Parallel computing | Approximate Bayesian inference | Generalized additive mixed models | Structured additive regression models | Gaussian Markov random fields | Sparse matrices | Laplace approximation | COUNT DATA | MAXIMUM-LIKELIHOOD | OPTIMAL-DESIGN | BINARY | STRUCTURED ADDITIVE REGRESSION | STATISTICS & PROBABILITY | STOCHASTIC VOLATILITY | LINEAR-MODELS | CHAIN MONTE-CARLO | MARKOV RANDOM-FIELDS | SPACE-TIME DATA | Markov processes | Monte Carlo method | Models | Algorithms | Analysis | Studies | Statistical methods | Bayesian analysis | Monte Carlo simulation
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5. Full Text Spatial data analysis with R-INLA with some extensions
by Bivand, Roger S and Gómez-Rubio, Virgilio and Rue, Håvard
Journal of Statistical Software, ISSN 1548-7660, 2015, Volume 63, Issue 20, pp. 1 - 31
The integrated nested Laplace approximation (INLA) provides an interesting way of approximating the posterior marginals of a wide range of Bayesian... 
INLA | Spatial statistics | NESTED LAPLACE APPROXIMATIONS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | spatial statistics | APPROXIMATE BAYESIAN-INFERENCE | MODELS | PACKAGE | STATISTICS & PROBABILITY
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6. Full Text Penalising model component complexity: A principled, practical approach to constructing priors
by Simpson, Daniel and Rue, Håvard and Riebler, Andrea and Martins, Thiago G and Sørbye, Sigrunn H
Statistical Science, ISSN 0883-4237, 02/2017, Volume 32, Issue 1, pp. 1 - 28
In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components,... 
Information geometry | Prior on correlation matrices | Interpretable prior distributions | Disease mapping | Bayesian theory | Hierarchical models | REGRESSION | CALIBRATION | interpretable prior distributions | INFORMATION | STATISTICS & PROBABILITY | PARAMETERS | prior on correlation matrices | VARIABLE SELECTION | APPROXIMATE BAYESIAN-INFERENCE | HORSESHOE ESTIMATOR | information geometry | PRIOR DISTRIBUTIONS | SENSITIVITY-ANALYSIS | hierarchical models | POSTERIOR CONCENTRATION | disease mapping | VDP | Matematikk og Naturvitenskap: 400 | Mathematics and natural science: 400 | Mathematics: 410 | Matematikk: 410
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7. Full Text Bayesian computing with INLA: New features
by Martins, Thiago G and Simpson, Daniel and Lindgren, Finn and Rue, Håvard
Computational Statistics and Data Analysis, ISSN 0167-9473, 11/2013, Volume 67, pp. 68 - 83
The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and... 
INLA | Latent Gaussian models | Approximate Bayesian inference | Approximate | Bayesian inference | NESTED LAPLACE APPROXIMATIONS | INFECTIONS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | LINEAR MIXED MODELS | STATISTICS & PROBABILITY | INFERENCE | Approximation
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8. Full Text A TOOLBOX FOR FITTING COMPLEX SPATIAL POINT PROCESS MODELS USING INTEGRATED NESTED LAPLACE APPROXIMATION (INLA)
by Janine B. Illian and Sigrunn H. Sørbye and Håvard Rue
The Annals of Applied Statistics, ISSN 1932-6157, 12/2012, Volume 6, Issue 4, pp. 1499 - 1530
This paper develops methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider models that... 
Datasets | Spatial behavior | Simulations | Ecological modeling | Spatial points | Approximation | Data models | Special Section on Mathematics of Planet Earth 2013 | Spatial models | Modeling | Parametric models | Model comparison | Cox processes | Marked point patterns | Model assessment | model comparison | MUSKOXEN | model assessment | INTENSITY | PATTERNS | STATISTICS & PROBABILITY | DIVERSITY | marked point patterns | INFERENCE | STRATEGIES | FOREST | Statistics - Applications | VDP | Statistics: 412 | Mathematics and natural science: 400 | Mathematics: 410 | Matematikk og Naturvitenskap: 400 | Matematikk: 410 | Statistikk: 412
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9. Full Text Bayesian inference for generalized linear mixed models
by Fong, Youyi and Rue, Håvard and Wakefield, Jon
Biostatistics, ISSN 1465-4644, 07/2010, Volume 11, Issue 3, pp. 397 - 412
Generalized linear mixed models (GLMMs) continue to grow in popularity due to their ability to directly acknowledge multiple levels of dependency and model... 
Longitudinal data | Prior specification | Penalized quasi-likelihood | Spline models | Integrated nested Laplace approximations | REGRESSION | HIERARCHICAL-MODELS | MATHEMATICAL & COMPUTATIONAL BIOLOGY | STATISTICS & PROBABILITY | Stochastic Processes | Computer Simulation | Humans | Bayes Theorem | Seizures - drug therapy | Epilepsy - drug therapy | Female | Linear Models | Longitudinal Studies | Breast Neoplasms - epidemiology | Cohort Studies
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10. Full Text Bayesian Spatial Modelling withR-INLA
by Lindgren, Finn and Rue, Håvard
Journal of Statistical Software, ISSN 1548-7660, 2015, Volume 63, Issue 19
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11. Full Text Fractional Gaussian noise: Prior specification and model comparison
: Fractional Gaussian noise
by Sørbye, Sigrunn Holbek and Rue, Håvard
Environmetrics, ISSN 1180-4009, 08/2018, Volume 29, Issue 5-6, p. e2457
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12. Full Text Penalised Complexity Priors for Stationary Autoregressive Processes
by Sørbye, Sigrunn Holbek and Rue, Håvard
Journal of Time Series Analysis, ISSN 0143-9782, 11/2017, Volume 38, Issue 6, pp. 923 - 935
The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior... 
robustness.JEL. C11; C18; C22; C88 | AR(p) | R‐INLA | prior selection | latent Gaussian models | R-INLA | C11 | C22 | C88 | robustness | STATISTICS & PROBABILITY | FRAILTY | C18 | INFERENCE | TRENDS | JEL | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MODELS | TIME-SERIES | BAYESIAN-ESTIMATION | Formulations | Computer simulation | Time series analysis | Bayesian analysis | Complexity | Personal computers | Autoregressive processes | VDP | AR( p) | Matematikk og Naturvitenskap: 400 | Mathematics and natural science: 400 | robustness. JEL. C11 | Mathematics: 410 | Matematikk: 410
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13. Full Text Tracking of multiple quantiles in dynamically varying data streams
by Hammer, Hugo Lewi and Yazidi, Anis and Rue, Håvard
Pattern Analysis and Applications, ISSN 1433-7541, 2019, Volume 23, Issue 1, pp. 225 - 237
In this paper we consider the problem of tracking multiple quantiles of dynamicallyvarying data stream distributions. The method is based on making incremental... 
Incremental algorithm | Quantile tracking | Multiple quantiles | Dynamic environment | Incremental algorithms | Dynamic environments
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14. Full Text Beyond the Valley of the Covariance Function
by Daniel Simpson and Finn Lindgren and Håvard Rue
Statistical Science, ISSN 0883-4237, 5/2015, Volume 30, Issue 2, pp. 164 - 166
Statistical Science 2015, Vol. 30, No. 2, 164-166 Discussion of "Cross-Covariance Functions for Multivariate Geostatistics" by Genton and Kleiber... 
STATISTICS & PROBABILITY | Statistics - Methodology
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15. Full Text Bayesian bivariate meta-analysis of diagnostic test studies with interpretable priors
: J. GUO, A. RIEBLER AND H. RUE
by Guo, Jingyi and Riebler, Andrea and Rue, Håvard
Statistics in Medicine, ISSN 0277-6715, 08/2017, Volume 36, Issue 19, pp. 3039 - 3058
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16. Full Text Fast Sampling of Gaussian Markov Random Fields
by Havard Rue
Journal of the Royal Statistical Society. Series B (Statistical Methodology), ISSN 1369-7412, 1/2001, Volume 63, Issue 2, pp. 325 - 338
This paper demonstrates how Gaussian Markov random fields (conditional autoregressions) can be sampled quickly by using numerical techniques for sparse... 
Linear systems | Geographic regions | Algorithms | Approximation | Disease models | Markov chains | Matrices | Coefficients | Covariance matrices | Cholesky factorization | Divide‐and‐conquer strategy | Conditional autoregressive model | Markov chain Monte Carlo methods | Block sampling | Gaussian Markov random field | Divide-and-conquer strategy | MODELS | GIBBS SAMPLER | STATISTICS & PROBABILITY | conditional autoregressive model | block sampling | divide-and-conquer strategy | BLOCKING | SCHEMES
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17. Full Text ESTIMATION AND EXTRAPOLATION OF TIME TRENDS IN REGISTRY DATA—BORROWING STRENGTH FROM RELATED POPULATIONS
by Andrea Riebler and Leonhard Held and Håvard Rue
The Annals of Applied Statistics, ISSN 1932-6157, 3/2012, Volume 6, Issue 1, pp. 304 - 333
To analyze and project age-specific mortality or morbidity rates ageperiod-cohort (APC) models are very popular. Bayesian approaches facilitate estimation and... 
Mortality | Random walk | Chronic obstructive pulmonary disease | Cantons | Modeling | Cohort effect | Parametric models | Age | Age groups | Forecasting models | INLA | Multivariate age-period-cohort model | Uniform correlation matrix | Projections | Bayesian analysis | projections | UNITED-STATES | multivariate age-period-cohort model | NESTED LAPLACE APPROXIMATIONS | CANCER INCIDENCE | STATISTICS & PROBABILITY | AIR-POLLUTION | uniform correlation matrix | PERIOD-COHORT MODELS | LEE-CARTER | MORTALITY TRENDS | SWITZERLAND | VITAL-RATES | AGE | Statistics - Applications
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18. Full Text Introduction to “Fast matrix computations for functional additive models” by S. Barthelmé
by Rue, Håvard and Rue, Håvard
Statistics and Computing, ISSN 0960-3174, 1/2015, Volume 25, Issue 1, pp. 45 - 45
Statistics and Computing/Statistics Programs | Artificial Intelligence (incl. Robotics) | Statistical Theory and Methods | Statistics | Probability and Statistics in Computer Science | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS
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19. Full Text Penalised Complexity Priors for Stationary Autoregressive Processes
: PC PRIORS FOR AR PROCESSES
by Sørbye, Sigrunn Holbek and Rue, Håvard
Journal of Time Series Analysis, ISSN 0143-9782, 11/2017, Volume 38, Issue 6, pp. 923 - 935
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20. Full Text Improving the INLA approach for approximate Bayesian inference for latent Gaussian models
by Ferkingstad, Egil and Rue, Håvard
Electronic Journal of Statistics, ISSN 1935-7524, 08/2015, Volume 9, Issue 2, pp. 2706 - 2731
We introduce a new copula-based correction for generalized linear mixed models (GLMMs) within the integrated nested Laplace approximation (INLA) approach for... 
Copulas | Latent Gaussian models | Generalized linear mixed models | Integrated nested laplace approximation | Bayesian computation | integrated nested Laplace approximation | LINEAR MIXED MODELS | LAPLACE APPROXIMATION | STATISTICS & PROBABILITY | latent Gaussian models | generalized linear mixed models | copulas
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