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N. Cressie 445challenge is to develop rich classes of loss functions that result in wise answersto important questions.AcknowledgementsI would like to thank Eddy Campbell for his comments on an earlier draft,Rui Wang for his help in preparing Figure 38.1, Emily Kang for her helpin preparing Figure 38.2, and Andrew Holder for his help in preparing themanuscript. This research was partially supported by the NASA Program,NNH11ZDA001N–OCO2 (Science Team for the OCO-2 Mission).ReferencesBanerjee, S., Carlin, B.P., and Gelfand, A.E. (2004). Hierarchical Modelingand Analysis for Spatial Data. ChapmanandHall/CRC,BocaRaton,FL.Banerjee, S., Gelfand, A.E., Finley, A.O., and Sang, H. (2008). Gaussianpredictive process models for large spatial data sets. Journal of the RoyalStatistical Society, Series B, 70:825–848.Barnett, V.D. (2004). Environmental Statistics: Methods and Applications.Wiley, New York.Bayes, T. (1763). An essay towards solving a problem in the doctrineof chances. Philosophical Transactions of the Royal Society of London,53:370–418.Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis.Springer, New York.Berliner, L.M. (1996). Hierarchical Bayesian time-series models. In MaximumEntropy and Bayesian Methods (K. Hanson and R. Silver, Eds.). Kluwer,Dordrecht, pp. 15–22.Chevallier, F., Bréon, F.-M., and Rayner, P.J. (2007). Contribution of theOrbiting Carbon Observatory to the estimation of CO2 sources and sinks:Theoretical study in a variational data assimilation framework. Journalof Geophysical Research, 112,doi:10.1029/2006JD007375.Connor, B.J., Boesch, H., Toon, G., Sen, B., Miller, C., and Crisp, D.(2008). Orbiting Carbon Observatory: Inverse method and prospective