Investigating the sensitivity of Gaussian processes to the choice of their correlation function and prior specifications

References

• O'Hagan , A. 1994 . Kendall's Advanced Theory of Statistics, Bayesian Inference , Vol. 2B , John Wiley & Sons, Inc. .
   Google Scholar
• O'Hagan , A. 1978 . Curve fitting and optimal design for prediction (with discussion) . Journal of the Royal Statistical Society Series B , 40 : 1 – 42 .
   Google Scholar
• Neal , R. 1999 . “ Regression and classification using gaussian process priors ” . In Bayesian Statistics Edited by: Bernardo , J. M. , Berger , J. O. , Dawid , A. P. and Smith , A. F.M. Vol. 6 , 69 – 95 .
   Google Scholar
• Diaconis , P. 1988 . “ Bayesian numerical analysis ” . In Statistical Decision Theory and Related Topics, IV , Edited by: Gupta , S. S. and Berger , J. Vol. 1 , 163 – 175 . New York : Wiley .
   Google Scholar
• O'Hagan , A. 1992 . “ Some Bayesian numerical analysis ” . In Bayesian Statistics Edited by: Bernardo , J. M. , Berger , J. O. , Dawid , A. P. and Smith , A. F.M. Vol. 4 , 345 – 363 .
   Google Scholar
• Kennedy , M. C. and O'Hagan , A. 2000 . Predicting the output from a complex computer code when fast approximations are available . Biometrika , 87 ( 1 ) : 1 – 13 .
   Web of Science ®Google Scholar
• Gomez Portugal Aguilar , D. , Litton , C. and O'Hagan , A. 2002 . Novel statistical model for a piece-wise linear radiocarbon calibration curve . Radiocarbon, , 44 : 195 – 212 .
   Google Scholar
• Schmidt , A. M. and O'Hagan , A. 2003 . Bayesian inference for non-stationary spatial covariance structure via spatial deformations . Journal of the Royal Statistical Society—Series B , 65 ( 3 ) : 743 – 758 .
   Google Scholar
• Banerjee , S. , Carlin , B. P. and Gelfand , A. E. 2004 . Hierarchical Modeling and Analysis for Spatial Data , CRC Press/Chapman Hall .
   Google Scholar
• Cressie , N. A.C. 1993 . Statistics for Spatial Data , Revised , New York : John Wiley and Sons, Inc. .
   Google Scholar
• Diggle , P. J. and Ribeiro Jr , P. J. 2000 . Model Based Geostatistics , Brazil : Associação Brasileira de Estatística-ABE-14 0 SINAPE .
   Google Scholar
• Stein , M. L. 1999 . Interpolation of Spatial Data , New York : Springer-Verlag .
   Google Scholar
• Schmidt , A. M. and Gelfand , A. E. 2003 . A Bayesian coregionalization model for multivariate pollutant data . Journal of Geophysics Research—Atmosphere , 108 ( D24 ) : 8783
   Web of Science ®Google Scholar
• Paez , M. S. , Gamerman , D. and De Oliveira , V. 2005 . Interpolation performance of a spatio-temporal model with spatially varying coefficients: application to PM10 concentration in Rio de Janeiro . Environmental and Ecological Statistics , 12 : 169 – 193 .
   Google Scholar
• Berger , J. O. , De Oliveira , V. and Sansó , B. 2001 . Objective Bayesian analysis of spatially correlated data . Journal of the American Statistical Association , 96 ( 456 ) : 1361 – 1374 .
   Web of Science ®Google Scholar
• Gamerman , D. and Lopes , H. F. 2006 . Markov Chain Monte Carlo—Stochastic Simulation for Bayesian Inference , 2 , London, , UK : Chapman & Hall .
   Google Scholar
• Agarwal , D. and Gelfand , A. E. 2005 . Slice Gibbs sampling for simulation based fitting of spatial models . Statistics and Computing , 15 : 61 – 69 .
   Web of Science ®Google Scholar
• Spiegelhalter , D. J. , Best , N. , Carlin , B. P. and van der Linde , A. 2002 . Bayesian measures of model complexity and fit (with discussion) . Journal of the Royal Statistical Society Series B , 64 : 583 – 639 .
   Google Scholar
• Gelfand , A. E. and Ghosh , S. K. 1998 . Model choice: a minimum posterior predictive loss approach . Biometrika , 85 : 1 – 11 .
   Web of Science ®Google Scholar
• Anderson , T. 1984 . An Introduction to Multivariate Statistical Analysis , John Wiley & Sons, Inc. .
   Google Scholar
• Spiegelhalter , D. , Thomas , A. , Best , N. and Gilks , W. 1996 . “ BUGS 0.5 Bayesian inference using Gibbs sampling—manual (version ii) ” . Cambridge, , UK : MRC Biostatistics Unit, Institute of Public Health . Technical Report
   Google Scholar