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All issues Volume 23 (2019) ESAIM: PS, 23 (2019) 271-309 References
Open Access
Issue
ESAIM: PS
Volume 23, 2019
Page(s) 271 - 309
DOI https://doi.org/10.1051/ps/2018023
Published online 17 June 2019
  1. M. Abramowitz and I.A. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Vol. 55 of Applied Mathematics Series. National Bureau of Standards (1964). [Google Scholar]
  2. E. Anderes, On the consistent separation of scale and variance for Gaussian random fields. Ann. Stat. 38 (2010) 870–893. [Google Scholar]
  3. B.C. Arnold, E. Castillo and J.M. Sarabia, Conditionally specified distributions: an introduction. Stat. Sci. 16 (2001) 268–269. [Google Scholar]
  4. J. Berger, The case for objective bayesian analysis. Bayesian Anal. 1 (2006) 385–402. [Google Scholar]
  5. J.O. Berger and J.M. Bernardo, On the development of reference priors. Bayesian Stat. 4 (1992) 35–60. [Google Scholar]
  6. J.O. Berger, V. De Oliveira and B. Sansó, Objective Bayesian analysis of spatially correlated data. J. Am. Stat. Assoc. 96 (2001) 1361–1374. [Google Scholar]
  7. J.O. Berger, J.M. Bernardo and D. Sun. Overall objective priors. Bayesian Anal. 10 (2015) 189–221. [Google Scholar]
  8. J.M. Bernardo, Reference analysis, in Vol. 25 of Handbook of Statistics, edited by D. Dey and C. Rao. Elsevier (2005) 17–90. [CrossRef] [Google Scholar]
  9. G. Celeux, J.-M. Marin and C. Robert, Sélection bayésienne de variables en régression linéaire. J. Soc. Fr. Statistique 147 (2005) 59–79. [Google Scholar]
  10. B.S. Clarke and A.R. Barron, Jeffreys’ prior is asymptotically least favorable under entropy risk. J. Stat. Plan. Inference 41 (1994) 37–60. [Google Scholar]
  11. A.P. Dawid and S.L. Lauritzen, Compatible prior distributions. Bayesian Methods with Applications to Science, Policy and Official Statistics, Selected Papers from ISBA 2000: The Sixth World Meeting of the International Society for Bayesian Analysis, edited by E.I. George. Eurostat, Luxembourg (2001) 109–118. [Google Scholar]
  12. A. Gelman and T.E. Raghunathan, Comment on “Conditionally specified distributions: an introduction” by B.C. Arnold, E. Castillo and J.M. Sarabia. Stat. Sci. 16 (2001) 268–269. [Google Scholar]
  13. M. Gu, X. Wang and J.O. Berger, Robust Gaussian stochastic process emulation. Ann. Stat. 46 (2018) 3038–3066. [Google Scholar]
  14. B.D. He, C.M. De Sa, I. Mitliagkas and C. Ré, Scan order in Gibbs sampling: Models in which it matters and bounds on how much. Adv.Neural Inf. Process. Syst. 29 (2016) 1–9. [Google Scholar]
  15. D. Heckerman, D.M. Chickering, C. Meek, R. Rounthwaite and C. Kadie, Dependency networks for inference, collaborative filtering, and data visualization. J. Mach. Learn. Res. 1 (2000) 49–75. [Google Scholar]
  16. J.P. Hobert and G. Casella, Functional compatibility, Markov chains, and Gibbs sampling with improper posteriors. J. Comput. Graph. Stat. 7 (1998) 42–60. [Google Scholar]
  17. H. Hotelling, New light on the correlation coefficient and its transforms. J. R. Stat. Soc. Ser. B (Methodol.) 15 (1953) 193–232. [Google Scholar]
  18. A.G. Journel and Ch. J. Huijbregts, Mining Geostatistics. Academic Press, New York (1978). [Google Scholar]
  19. H. Kazianka and J. Pilz, Objective bayesian analysis of spatial data with uncertain nugget and range parameters. Can. J. Stat. 40 (2012) 304–327. [Google Scholar]
  20. M.C. Kennedy and A. O’Hagan. Bayesian calibration of computer models. J. R. Stat. Soc., Ser. B (Stat. Methodol.) 63 (2001) 425–464. [Google Scholar]
  21. K.L. Kuo and Y.J. Wang, Pseudo-Gibbs sampler for discrete conditional distributions. Ann. Inst. Stat. Math. (2017) 1–13. [Google Scholar]
  22. K.-L. Kuo, C.-C. Song and T.J. Jiang, Exactly and almost compatible joint distributions for high-dimensional discrete conditional distributions. J. Multivar. Anal. 157 (2017) 115–123. [Google Scholar]
  23. R. Li and A. Sudjianto, Analysis of computer experiments using penalized likelihood in Gaussian Kriging models. Technometrics 47 (2005) 111–120. [Google Scholar]
  24. I. Mitliagkas and L. Mackey, Improving Gibbs Sampler Scan Quality with DoGS, in Vol. 70 of Proceedings of the 34th International Conference on Machine Learning. (2017) 2469–2477. [Google Scholar]
  25. R. Paulo, Default priors for Gaussian processes. Ann. Stat. 33 (2005) 556–582. [Google Scholar]
  26. C.E. Rasmussen and C.K.I. Williams, Gaussian Processes for Machine Learning. MIT Press (2006). [Google Scholar]
  27. C. Ren, D. Sun and C. He, Objective bayesian analysis for a spatial model with nugget effects. J. Stat. Plan. Inference 142 (2012) 1933–1946. [Google Scholar]
  28. C. Ren, D. Sun and S.K. Sahu, Objective bayesian analysis of spatial models with separable correlation functions. Can. J. Stat. 41 (2013) 488–507. [Google Scholar]
  29. C.P. Robert, The Bayesian Choice : From Decision-Theoretic Foundations to Computational Implementation. Springer-Verlag, New York (2007). [Google Scholar]
  30. A. Roverato and G. Consonni, Compatible prior distributions for directed acyclic graph models. J. R. Stat. Soc., Ser. B (Stat. Methodol.) 66 (2004) 47–61. [CrossRef] [Google Scholar]
  31. T.J. Santner, B.J. Williams and W.I. Notz, The Design and Analysis of Computer Experiments. Springer-Verlag, New York (2003). [Google Scholar]
  32. M.L. Stein, Interpolation of Spatial Data. Some Theory for Kriging. Springer Series in Statistics. Springer-Verlag, New York (1999). [Google Scholar]
  33. R. Yang and J.O. Berger, A Catalog of Noninformative Priors. Institute of Statistics and Decision Sciences, Duke University (1996). [Google Scholar]
  34. B. Yet and W. Marsh, Compatible and incompatible abstractions in Bayesian networks. Knowl.-Based Syst. 62 (2014) 84–97. [CrossRef] [Google Scholar]
  35. H. Zhang, Inconsistent Estimation and Asymptotically Equal Interpolations in Model-based Geostatistics. J. Am. Stat. Assoc. 99 (2004) 250–261. [Google Scholar]

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