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References

Published online by Cambridge University Press:  05 November 2015

Jo Eidsvik
Affiliation:
Norwegian University of Science and Technology, Trondheim
Tapan Mukerji
Affiliation:
Stanford University, California
Debarun Bhattacharjya
Affiliation:
IBM T. J. Watson Research Center, New York
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Summary

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Type
Chapter
Information
Value of Information in the Earth Sciences
Integrating Spatial Modeling and Decision Analysis
, pp. 365 - 377
Publisher: Cambridge University Press
Print publication year: 2015

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References

Adams, R. M., Bryant, K. J., McCarl, B. A., Legler, D. M., O’Brien, J., Solow, A., and Weiher, R., (1995). Value of improved long-range weather information. Contemporary Economic Policy, 13, 1019.CrossRefGoogle Scholar
Alemu, E. T., Palmer, R. N., Polebitski, A., and Meaker, B. (2011). Decision support system for optimizing reservoir operations using ensemble streamflow predictions. Journal of Water Resources Planning and Management, 137, 7282.CrossRefGoogle Scholar
Alkhatib, A., and King, P. (2014). An approximate dynamic programming approach to decision making in the presence of uncertainty for surfactant-polymer flooding. Computational Geosciences, 18, 243263.CrossRefGoogle Scholar
Allard, D., and Naveau, P. (2007). Simulating and analyzing spatial skew normal random fields. Communications in Statistics, 36, 18211834.CrossRefGoogle Scholar
Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis. New York: Wiley.Google Scholar
Armstrong, M., Galli, A. G., Beucher, H., Le Loc’h, G., Renard, D., Dogliez, B., Eschard, R., and Geffroy, F. (2011). Plurigaussian Simulations in Geosciences, 2nd edn. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Arpat, B., and Caers, J. (2007). Conditional simulations with patterns. Mathematical Geology, 39, 177203.CrossRefGoogle Scholar
Ash, R. B. (1965). Information Theory. New York: Dover Publications.Google Scholar
Auken, E., Chistiansen, A., Jacobsen, L., and Sørensen, K. (2008). A resolution study of buried valleys using laterally constrained inversion of TEM data. Journal of Applied Geophysics, 65, 1020.CrossRefGoogle Scholar
Avseth, P., Mukerji, T., Jørstad, A., Mavko, G., and Veggeland, T. (2001). Seismic reservoir mapping from 3-D AVO in a North Sea turbidite system. Geophysics, 66, 11571176.CrossRefGoogle Scholar
Avseth, P., Mukerji, T., and Mavko, G. (2005). Quantitative Seismic Interpretation. Cambridge University Press.CrossRefGoogle Scholar
Bachrach, R. (2006). Joint estimation of porosity and saturation using stochastic rock-physics modelling. Geophysics, 71, O53O63.CrossRefGoogle Scholar
Ballari, D., de Bruin, S., and Bregt, A. K. (2012). Value of information and mobility constraints for sampling with mobile sensors. Computers & Geosciences, 49, 102111.CrossRefGoogle Scholar
Banerjee, S., Gelfand, A. E., Finley, A. O., and Sang, H. (2008). Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society, Series B, 70, 825848.CrossRefGoogle ScholarPubMed
Banerjee, S., Gelfand, A., and Carlin, B. (2004). Hierachical Modeling and Analysis for Spatial Data. Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Bardossy, A., and Li, J. (2008). Geostatistical interpolation using copulas. Water Resources Research, 44, W07412.CrossRefGoogle Scholar
Barros, E. G. D., Jansen, J. D., and van den Hof, P. M. J. (2014). Value of information in closed loop reservoir management. Extended abstract, 14th European Conference on the Mathematics of Oil Recovery (ECMOR), Catania, Italy.CrossRefGoogle Scholar
Bates, M. E., Sparrevik, M., de Lichy, N., and Linkov, I. (2014). The value of information for managing contaminated sediments. Environmental Science and Technology, 48, 94789485.CrossRefGoogle ScholarPubMed
Beaumont, M., Zhang, W., and Balding, D. (2002). Approximate Bayesian computation in population genetics. Genetics, 162, 20252032.CrossRefGoogle ScholarPubMed
Bergseng, E., Ørka, H. O., Næsset, E., and Gobakken, T. (2015). Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources. Annals of Forest Science, 72, 3345.CrossRefGoogle Scholar
Bertsekas, D. P. (2012). Dynamic Programming and Optimal Control. Vol. II of Approximate Dynamic Programming, 4th edn. Athena Scientific.Google Scholar
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B, 36, 192236.Google Scholar
Besag, J. (1986). On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B, 48, 259302.Google Scholar
Bhattacharjya, D., and Deleris, L. (2014). The value of information in some variations of the stopping problem. Decision Analysis, 11, 189203.CrossRefGoogle Scholar
Bhattacharjya, D., Eidsvik, J., and Mukerji, T. (2010). The value of information in spatial decision making. Mathematical Geosciences, 42, 141163.CrossRefGoogle Scholar
Bhattacharjya, D., Eidsvik, J., and Mukerji, T. (2013). The value of information in portfolio problems with dependent projects. Decision Analysis, 10, 341351.CrossRefGoogle Scholar
Bhattacharjya, D., and Mukerji, T. (2006). Using influence diagrams to analyze decisions in 4D seismic reservoir monitoring. The Leading Edge, 25, 12361239.CrossRefGoogle Scholar
Bhattacharjya, D., and Shachter, R. (2007). Evaluating influence diagrams with decision circuits. In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI), AUAI Press, pp. 9–16.Google Scholar
Bhattacharjya, D., and Shachter, R. (2008). Sensitivity analysis in decision circuits. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI), AUAI Press, pp. 34–42.Google Scholar
Bhattacharjya, D., and Shachter, R. (2010). Three new sensitivity analysis methods for influence diagrams. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI), AUAI Press, pp. 56–64.Google Scholar
Bickel, J. E. (2008). The relationship between perfect and imperfect information in a two-action risk-sensitive problem. Decision Analysis, 5, 116128.CrossRefGoogle Scholar
Bickel, J. E., Gibson, R. L., McVay, D. A., Pickering, S., and Waggoner, J. (2008a). Quantifying 3D land seismic reliability and value. Society of Petroleum Engineers: Reservoir Evaluation and Engineering, 11, 832841.Google Scholar
Bickel, J. E., and Smith, J. E. (2006). Optimal sequential exploration: a binary learning model. Decision Analysis, 3, 1632.CrossRefGoogle Scholar
Bickel, J. E., Smith, J. E., and Meyer, J. L. (2008b). Modeling dependence among geological risks in sequential exploration decisions. Society of Petroleum Engineers: Reservoir Evaluation & Engineering, 11, 233251.Google Scholar
Bielza, C., Gomez, M., and Shenoy, P. (2010). Modeling challenges with influence diagrams: constructing probability and utility models. Decision Support Systems, 49, 354364.CrossRefGoogle Scholar
Bielza, C., and Shenoy, P. (1999). A comparison of graphical techniques for asymmetric decision problems. Management Science, 45, 15521569.CrossRefGoogle Scholar
Blackwell, D. (1953). Equivalent comparisons of experiments. Annals of Mathematical Statistics, 24, 265272.CrossRefGoogle Scholar
Blangy, J. P., Schiott, C., Vejbaek, O., and Maguire, D. (2014). The value of 4D seismic: has the promise been fulfilled? Society of Exploration Geophysics Annual Meeting, 2552–2557.CrossRefGoogle Scholar
Borg, I., and Groenen, P. J. F. (2005). Modern Multidimensional Scaling: Theory and Applications, 2nd edn. New York: Springer-Verlag.Google Scholar
Borgault, G. (1997). Using non-Gaussian distributions in geostatistical simulation. Mathematical Geology, 29, 315334.CrossRefGoogle Scholar
Borgos, H. G., Omre, H., and Townsend, C. (2002). Size distribution of geological faults: model choice and parameter estimation. Statistical Modeling, 2, 217234.CrossRefGoogle Scholar
Borisova, T., Shortle, J., Horan, R. D., and Abler, D. (2005). Value of information for water quality management. Water Resources Research, 41, W06004.CrossRefGoogle Scholar
Borsuk, M., Clemen, R., Maguire, L., and Reckhow, K. (2001). Stakeholder values and scientific modeling in the Neuse River watershed. Group Decision and Negotiation, 10, 355373.CrossRefGoogle Scholar
Bouma, J. A., van der Woerd, H. J., and Kuik, O. J. (2009). Assessing the value of information for water quality management in the North Sea. Journal of Environmental Management, 90, 12801288.CrossRefGoogle ScholarPubMed
Braithwaite, R. S., and Scotch, M. (2013). Using value of information to guide evaluation of decision support for differential diagnosis: is it time for a new look? BMS Medical Informatics and Decision Making, 13, 105.CrossRefGoogle ScholarPubMed
Bratvold, R. B., and Begg, S. H. (2010). Making Good Decisions. Richardson, TX: Society of Petroleum Engineers.CrossRefGoogle Scholar
Bratvold, R. B., Bickel, J. E., Lohne, H. P. (2009). Value of information in the oil and gas industry: Past, present, and future. Society of Petroleum Engineers: Reservoir Evaluation & Engineering, 12, 630638.Google Scholar
Breslow, N. E., and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 925.Google Scholar
Brockwell, P. J., and Davis, R. A. (2009). Time Series: Theory and Methods. New York: Springer-Verlag.Google Scholar
Brown, D. B and Smith, J. E. (2013). Optimal sequential exploration: bandits, clairvoyants, and wildcats. Operations Research, 60, 262274.Google Scholar
Bruland, O., Færevåg, Å., Steinsland, I., Listen, G. E., and Sand, K. (2015). Weather SDM: estimating snow density with high precision using snow depth and local climate. Hydrology Research.CrossRefGoogle Scholar
Byerlee, D., and Anderson, J. R. (1982). Risk, utility and the value of information in farmer decision making. Review of Marketing and Agricultural Economics, 50, 231246.Google Scholar
Cabrera, V. E., Letson, D., and Podesta, G. (2007). The value of climate information when farm programs matter. Agricultural Systems, 93, 2542.CrossRefGoogle Scholar
Caers, J. (2005). Petroleum Geostatistics. Richardson, TX: Society of Petroleum Engineers.CrossRefGoogle Scholar
Caers, J. (2011). Modeling Uncertainty in the Earth Sciences. Chichester, UK: Wiley-Blackwell.CrossRefGoogle Scholar
Carlin, B. P., and Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Chan, G., and Wood, A. T. A. (1997). An algorithm for simulating stationary Gaussian random fields. Applied Statistics, 46, 171181.Google Scholar
Chickering, M. (1996). Learning Bayesian networks is NP-complete. In D. Fisher and H. Lenz, eds., Learning from Data: Artificial Intelligence and Statistics V, New York: Springer-Verlag, pp. 121–130.CrossRefGoogle Scholar
Chiles, J. P., and Delfiner, P. (2012). Geostatistics: Modeling Spatial Uncertainty, 2nd edn. New York: Wiley.CrossRefGoogle Scholar
Christie, M. A., and Blunt, M. J. (2001). Tenth SPE comparative solution project: a comparison of upscaling techniques. Society of Petroleum Engineers: Reservoir Engineering & Evaluation, 4, 308317.Google Scholar
Clemen, R., and Reilly, T. (1999). Making Hard Decisions with Decision Tools Suite. Pacific Grove, CA: Duxbury Press.Google Scholar
Clemen, R., and Winkler, R. (1985). Limits for the precision and value of information from dependent sources. Operations Research, 33, 427442.CrossRefGoogle Scholar
Covaliu, Z., and Oliver, R. M. (1995). Representation and solution of decision problems using sequential decision diagrams. Management Science, 41, 18601881.CrossRefGoogle Scholar
Cover, T., and Thomas, J. (2006). Elements of Information Theory, 2nd edn. New York: Wiley-Interscience.Google Scholar
Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J. (2007). Probabilistic Networks and Expert Systems. New York: Springer-Verlag.Google Scholar
Cox, T. F., and Cox, M. A. A. (2001). Multidimensional Scaling, 2nd edn. Boca Raton, FL: Chapman and Hall/CRC.Google Scholar
Cressie, N. (1993). Statistics for Spatial Data. New York: Wiley.CrossRefGoogle Scholar
Cressie, N., and Johannesson, G. (2008). Fixed rank Kriging for very large spatial data sets. Journal of the Royal Statistical Society, Series B, 70, 209226.CrossRefGoogle Scholar
Cressie, N., and Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. New York: Wiley.Google Scholar
Curtis, A. (2004). Theory of model-based geophysical survey and experimental design: Part A – Linear problems. The Leading Edge, 23, 9971004.CrossRefGoogle Scholar
Daly, C., and Caers, J. (2010). Multi-point geostatistics – An introductory overview. First Break, 28, 3947.CrossRefGoogle Scholar
Darwiche, A. (2009). Modeling and Reasoning with Bayesian Networks. Cambridge University Press.CrossRefGoogle Scholar
de Bruin, S., Bregt, A., and van de Ven, M. (2001). Assessing fitness for use: the expected value of spatial data sets. International Journal of Geographical Information Science, 15, 457471.CrossRefGoogle Scholar
Demidenko, E. (2004). Mixed Models: Theory and Applications. New York: Wiley.CrossRefGoogle Scholar
Deutsch, C., and Journel, A. G. (1992). GSLIB: Geostatistical Software Library and User’s Guide. New York: Oxford University Press.Google Scholar
Diggle, P., and Lophaven, S. (2006). Bayesian geostatistical design. Scandinavian Journal of Statistics, 33, 5364.CrossRefGoogle Scholar
Diggle, P. J., Tawn, J. A., and Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society, Series C, 47, 299350.CrossRefGoogle Scholar
Dobbie, M. J., Henderson, B. L., and Stevens, D. L. (2008). Sparse sampling: spatial design for monitoring stream networks. Statistics Surveys, 2, 113153.CrossRefGoogle Scholar
Dobson, A. J., and Barnett, A. (2008). An Introduction to Generalized Linear Models. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Dominguez-Molina, J., Gonzalez-Farias, G., and Gupta, A. (2003). The Multivariate Closed Skew Normal Distribution, Technical report 03-12, Department of Mathematics and Statistics, Bowling Green University.Google Scholar
Doucet, A., de Freitas, N., and Gordon, N. (2001). Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag.CrossRefGoogle Scholar
Dvorkin, J., and Nur, A. (1996). Elasticity of high-porosity sandstones: theory for two North Sea datasets. Geophysics, 61, 13631370.CrossRefGoogle Scholar
Dyer, J. S., and Sarin, R. (1982). Relative risk aversion. Management Science, 28, 875886.CrossRefGoogle Scholar
Edwards, W, Miles, R., and von Winterfeldt, D. (2007). Introduction. In Edwards, W., Miles, R., and von Winterfeldt, D., eds., Advances in Decision Analysis: From Foundations to Applications. Cambridge University Press, pp. 112.CrossRefGoogle Scholar
Efron, B., and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Efros, A. A., and Freeman, W. T. (2001). Image quilting for texture synthesis and transfer. In Proceedings of the ACM SIGGRAPH Conference on Computer Graphics, pp. 341–346.CrossRefGoogle Scholar
Eidsvik, J., Avseth, P., Omre, H., Mukerji, T., and Mavko, G. (2004a). Stochastic reservoir characterization using prestack seismic data. Geophysics, 69, 978993.CrossRefGoogle Scholar
Eidsvik, J., Bhattacharjya, D., and Mukerji, T. (2008). Value of information of seismic amplitude and CSEM resistivity. Geophysics, 73, R59R69.CrossRefGoogle Scholar
Eidsvik, J., and Ellefmo, S. L. (2013). The value of information in mineral exploration within a multi-Gaussian framework. Mathematical Geosciences, 45, 777798.CrossRefGoogle Scholar
Eidsvik, J., Mukerji, T., and Switzer, P. (2004b). Estimation of geological attributes from a well log: an application of hidden Markov chains. Mathematical Geology, 36, 379397.CrossRefGoogle Scholar
Ellefmo, S. L., and Eidsvik, J. (2009). Local and spatial joint frequency uncertainty and its application to rock mass characterisation. Rock Mechanics and Rock Engineering, 42, 667688.CrossRefGoogle Scholar
Evangelou, E., and Eidsvik, J. (2015). The Value of Information for Correlated GLMs, Technical report 2/2015, Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU).Google Scholar
Evangelou, E., and Zhu, Z. (2012). Optimal predictive design augmentation for spatial generalised linear mixed models. Journal of Statistical Planning and Inference, 142, 32423253.CrossRefGoogle Scholar
Evensen, G. (2009). Data Assimilation: The Ensemble Kalman Filter. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Farquhar, P. (1984). State of the art – utility assessment methods. Management Science, 30, 12831300.CrossRefGoogle Scholar
Ferreira, M. A. R., and Lee, H. K. H. (2007). Multiscale Modeling: A Bayesian Perspective. New York: Springer-Verlag.Google Scholar
Forsberg, O. I., and Guttormsen, A. G. (2006). The value of information in salmon farming: harvesting the right fish at the right time. Aquaculture Economics and Management, 10, 183200.CrossRefGoogle Scholar
Frazier, P. I., and Powell, W. B. (2010). Paradoxes in learning and the marginal value of information. Decision Analysis, 7, 378403.CrossRefGoogle Scholar
Friedman, J., Hastie, T., and Tibshirani, R. (2009). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432441.CrossRefGoogle Scholar
Froidevaux, R. (1992). Probability field simulation. In A. Soares, ed., Geostatistics Tróia, Proceedings of the 4th International Geostatistical Congress, Kluwer Academic Publishers.Google Scholar
Froyland, G., Menabde, M., Stone, P., and Hodson, D. (2004). The value of additional drilling to open pit mining projects. In Proceedings of Orebody Modelling and Strategie Mine Planning – Uncertainty and Risk Management, Perth, Australia, pp. 169–176.Google Scholar
Fuentes, M., Chaudhuri, A., and Holland, D. M. (2007). Bayesian entropy for spatial sampling design of environmental data. Environmental and Ecological Statistics, 14, 323340.CrossRefGoogle Scholar
Gaetan, C., and Guyon, X. (2010). Spatial Statistics and Modelling. New York: Springer-Verlag.CrossRefGoogle Scholar
Gamerman, D., and Lopes, H. F. (2006). Markov Chain Monte Carlo. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Gelfand, A. E., Kottas, A., and MacEachern, S. N. (2005). Bayesian nonparametric spatial modeling with Dirichlet mixing. Journal of the American Statistical Association, 100, 10211035.CrossRefGoogle Scholar
Geman, S., and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721741.CrossRefGoogle ScholarPubMed
Genz, A., and Bretz, F. (2009). Computation of Multivariate Normal and T Probabilities: Lecture Notes in Statistics. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Ginsbourger, D., Rosspopoff, B., Pirot, G., Durrande, N., and Renard, P. (2013). Distance-based Kriging relying on proxy simulations for inverse conditioning. Advances in Water Resources, 52, 275291.CrossRefGoogle Scholar
Gneiting, T., and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102, 359378.CrossRefGoogle Scholar
Golub, G. H., and van Loan, C. F. (1996). Matrix Computations. Baltimore, MD: John Hopkins University Press.Google Scholar
Gomez, C. T., Dvorkin, J., and Mavko, G. (2008). Estimating the hydrocarbon volume from elastic and resistivity data: a concept. The Leading Edge, 27, 710718.CrossRefGoogle Scholar
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. New York: Oxford University Press.CrossRefGoogle Scholar
Gramacy, R. B., and Apley, D. W. (2015). Local Gaussian process approximation for large computer experiments. Journal of Computational and Graphical Statistics.CrossRefGoogle Scholar
Gray, R. M. (2006). Toeplitz and circulant matrices: a review. Foundations and Trends in Communication and Information Theory, 2, 155239.CrossRefGoogle Scholar
Grayson, C. J., Jr. (1960). Decisions Under Uncertainty: Drilling Decisions by Oil and Gas Operators. Cambridge, MA: Harvard University Press.Google Scholar
Green, P. J., Hjort, N. L., and Richardson, S. (eds.) (2003). Highly Structured Stochastic Systems. New York: Oxford University Press.CrossRefGoogle Scholar
Guardiano, F., and Srivastava, R. (1993). Multivariate geostatistics: beyond bivariate moments. In Soares, A., ed., Geostatistics Tróia, Proceedings of the 4th International Geostatistical Congress, Kluwer Academic Publishers, pp. 133144.CrossRefGoogle Scholar
Hansen, G. J. A., and Jones, M. L. (2008). The value of information in fishery management. Fisheries, 33, 340348.CrossRefGoogle Scholar
Hantschel, T., and Kauerauf, A. I. (2009). Fundamentals of Basin and Petroleum Systems Modelling. Berlin: Springer-Verlag.Google Scholar
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning. New York: Springer-Verlag.CrossRefGoogle Scholar
Heckerman, D., Horvitz, E., and Nathwani, B. (1989). Update on the pathfinder project. In Proceedings of the 13th Symposium on Computer Applications in Medical Care, IEEE Computer Society Press, pp. 203–207.Google Scholar
Heckerman, D., and Shachter, R. (1995). Decision-theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research, 3, 405430.CrossRefGoogle Scholar
Hilton, R. (1981). The determinants of information value: synthesizing some general results. Management Science, 27, 5764.CrossRefGoogle Scholar
Horesh, L., Haber, E., and Tenorio, L. (2010). Optimal experimental design for the large-scale nonlinear ill-posed problem of impedance imaging. In L. Biegler G. Biros, O. Ghattas, M. Heinkenschloss, D. Keyes, B. Mallick, L. Tenorio, B. van Bloemen Waanders, K. Willcox, and Y. Marzouk, eds., Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley, pp. 273–290.CrossRefGoogle Scholar
Houck, R. T. (2007). Time-lapse seismic repeatability – how much is enough? The Leading Edge, 26, 828834.CrossRefGoogle Scholar
Houck, R. T., and Pavlov, D. A. (2006). Evaluating reconnaissance CSEM survey designs using detection theory. The Leading Edge, 25, 9941004.CrossRefGoogle Scholar
Howard, R. (1964). Decision analysis: applied decision theory. In Proceedings of the 4th International Conference on Operational Research, Wiley-Interscience, pp. 55–71.Google Scholar
Howard, R. (1966). Information value theory. IEEE Transactions on Systems Science and Cybernetics, 2, 2226.CrossRefGoogle Scholar
Howard, R. (1967). Value of information lotteries. IEEE Transactions on Systems Science and Cybernetics, 3, 5460.CrossRefGoogle Scholar
Howard, R. (1971). Proximal decision analysis. Management Science, 17, 507541.CrossRefGoogle Scholar
Howard, R. (2007). The foundations of decision analysis revisited. In Edwards, W., Miles, R., and von Winterfeldt, D., eds., Advances in Decision Analysis: From Foundations to Applications. Cambridge University Press, pp. 3256.CrossRefGoogle Scholar
Howard, R. A., and Abbas, A. (2015). Foundations of Decision Analysis. London: Pearson Education.Google Scholar
Howard, R., and Matheson, J. (1984). Influence diagrams. In Howard, R. and Matheson, J., eds., The Principles and Applications of Decision Analysis, Vol. II. Strategic Decisions Group, pp. 721762.Google Scholar
Illian, J., Penttinen, A., Stoyan, H., and Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. Chichester, UK: Wiley.Google Scholar
Isaaks, E. H., and Srivastava, R. M. (1989). An Introduction to Applied Geostatistics. Oxford: Oxford University Press.Google Scholar
Izenman, A. J. (2008). Modern Multivariate Statistical Techniques. New York: Springer-Verlag.CrossRefGoogle Scholar
Jaakkola, T. S. (2000). Tutorial on variational approximation methods. In M. Opper and D. Saad, eds., Advanced Mean Field Methods. MIT Press, pp. 129–159.Google Scholar
Jensen, F., Jensen, F. V., and Dittmer, S. (1994). From influence diagrams to junction trees. In R. Lopez de Mantaras and D. Poole, eds., Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann, pp. 367–373.CrossRefGoogle Scholar
Jensen, F. V. (1996). An Introduction to Bayesian Networks. London: UCL Press.Google Scholar
Jensen, F. V., and Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs, 2nd edn. New York: Springer-Verlag.CrossRefGoogle Scholar
Jensen, J. L., Lake, L. W., Corbett, P. W. M., and Goggin, D. J. (2000). Statistics for Petroleum Engineers and Geoscientists, 2nd edn. Amsterdam: Elsevier.Google Scholar
Joe, H. (2014). Dependence Modeling with Copulas. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Johnson, N. L., Kotz, S., and Balakrishnan, N. (1994). Continuous Univariate Distributions. Hoboken, NJ: Wiley.Google Scholar
Johnson, N. L., Kotz, S., and Balakrishnan, N. (1997). Discrete Multivariate Distributions. Hoboken, NJ: Wiley.Google Scholar
Johnson, R. A., and Wichern, D. W. (2012). Applied Multivariate Statistical Analysis. Delhi: Phi Learning Private Limited.Google Scholar
Journel, A. G. (1983). Nonparametric estimation of spatial distributions. Mathematical Geology, 15, 445468.CrossRefGoogle Scholar
Journel, A. G., and Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press. Reprint by Blackburn Press, Caldwell, NJ, 2004.Google Scholar
Kangas, A. S. (2010). The value of forest information. European Journal of Forest Research, 129, 863874.CrossRefGoogle Scholar
Karam, K. S., Karam, J. S., and Einstein, H. H. (2007). Decision analysis applied to tunnel exploration planning I: principles and case study. Journal of Construction Engineering and Management, 133, 344353.CrossRefGoogle Scholar
Kaufman, G. (1993). Statistical issues in the assessment of undiscovered oil and gas resources. The Energy Journal, 14, 183215.CrossRefGoogle Scholar
Kazianka, H., and Pilz, J. (2011). Bayesian spatial modeling and interpolation using copulas. Computers & Geosciences, 37, 310319.CrossRefGoogle Scholar
Keeney, R. (2007). Developing objectives and attributes. In Edwards, W., Miles, R., and von Winterfeldt, D., eds., Advances in Decision Analysis: From Foundations to Applications. Cambridge University Press, pp. 104128.CrossRefGoogle Scholar
Keeney, R., and Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Hoboken, NJ: Wiley.Google Scholar
Keisler, J. M. (2004). Value of information in portfolio decision analysis. Decision Analysis, 1, 177189.CrossRefGoogle Scholar
Keisler, J. M. (2005). Additivity of information in two-act linear loss decisions with normal priors. Risk Analysis, 25, 351359.CrossRefGoogle ScholarPubMed
Keisler, J. M., and Brodfuehrer, M. (2009). An application of value-of-information to decision process reengineering. Engineering Economist, 54, 197221.CrossRefGoogle Scholar
Keisler, J. M., Collier, Z., Chu, E., Sinatra, N., and Linkov, I. (2014). Value of information analysis: the state of application. Environment Systems and Decisions, 34, 323.CrossRefGoogle Scholar
Kelkar, M., and Perez, G. (2002). Applied Geostatistics for Reservoir Characterization. Richardson, TX: Society of Petroleum Engineers.CrossRefGoogle Scholar
Khare, K., Oh, S. Y., and Rajaratnam, B. (2015). A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees. Journal of the Royal Statistical Society, Series B.CrossRefGoogle Scholar
Kim, H. M., and Mallick, B. K. (2004). A Bayesian prediction using the skew Gaussian distribution. Journal of Statistical Planning and Inference, 120, 85101.CrossRefGoogle Scholar
Kirkwood, C. (1993). An algebraic approach to formulating and solving large models for sequential decisions under uncertainty. Management Science, 39, 900913.CrossRefGoogle Scholar
Kirkwood, C. (2004). Approximating risk aversion in decision analysis applications. Decision Analysis, 1, 5167.CrossRefGoogle Scholar
Kolbjørnsen, O., Hauge, R., Drange-Espeland, M., and Buland, A. (2012). Model-based fluid factor for controlled source electromagnetic data. Geophysics, 77, E21E31.CrossRefGoogle Scholar
Koller, D., and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: MIT Press.Google Scholar
Konishi, C. (2014). Evaluation of uncertainty and risk of CO2 sequestration with stochastic models conditioned by seismic and well data. MSc thesis, Stanford University.Google Scholar
Kontoghiorghes, E. J. (ed.) (2005). Handbook of Parallel Computing and Statistics. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Kotz, S., Johnson, N. L., and Balakrishnan, N. (2000). Continuous Multivariate Distributions. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Krause, A., and Guestrin, C. (2007). Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach. In International Conference on Machine Learning. Omnipress, pp. 449–456.CrossRefGoogle Scholar
Krause, A., and Guestrin, C. (2009). Optimal value of information in graphical models. Journal of Artificial Intelligence Research, 35, 557591.CrossRefGoogle Scholar
Lantuejoul, C. (2002). Geostatistical Simulations: Models and Algorithms. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Lauritzen, S. L., and Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their applications to expert systems. Journal of the Royal Statistical Society, Series B, 50, 157224.Google Scholar
Lawler, G. F. (2006). Introduction to Stochastic Processes. Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Le, N. D., and Zidek, J. V. (2006). Statistical Analysis of Environmental Space-Time Processes. New York: Springer-Verlag.Google Scholar
Lichtenstein, S., and Slovic, P. (2006). The Construction of Preference. Cambridge University Press.CrossRefGoogle Scholar
Lie, K. A., Krogstad, S., Ligaarden, I. S., Natvig, J. R., Nilsen, H. M., and Skaflestad, B. (2012). Open-source MATLAB implementation of consistent discretisations on complex grids. Computational Geosciences, 16, 297322.CrossRefGoogle Scholar
Lilleborge, M., Hauge, R., and Eidsvik, J. (in press). Information gathering in Bayesian networks applied to petroleum prospecting. Mathematical Geosciences.Google Scholar
Lindberg, D. V., and Lee, H. K. H. (2015). Optimization under constraints by applying an asymmetric entropy measure. Journal of Computational and Graphical Statistics, 24, 379–393.CrossRefGoogle Scholar
Lindgren, F., Rue, H., and Lindstrøm, J. (2011). An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society, Series B, 73, 423498.CrossRefGoogle Scholar
Liu, J. L. (2001). Monte Carlo Strategies in Scientific Computing. New York: Springer-Verlag.Google Scholar
Lumley, D. (2001). Time-lapse seismic reservoir monitoring. Geophysics, 66, 5053.CrossRefGoogle Scholar
Lumley, D., Behrens, R. A., and Wang, Z. (1997). Assessing the technical risk of a 4-D seismic project. The Leading Edge, 16, 12871292.CrossRefGoogle Scholar
Lundberg, A., Granlund, N., and Gustafsson, D. (2010). Towards automated ‘Ground truth’ snow measurements – a review of operational and new measurement methods for Sweden, Norway, and Finland. Hydrological Processes, 24, 19551970.CrossRefGoogle Scholar
MacDonald, I. L., and Zucchini, W. (1997). Hidden Markov and Other Models for Discrete-Valued Time Series. Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Manoharan, N. (2014). K-nearest neighbour methods for value of information in petroleum decision making. MSc thesis, Norwegian University of Science and Technology (NTNU).Google Scholar
Mantyniemi, S., Kuikka, S., Rahikainen, M., Kell, L. T., and Kaitala, V. (2009). The value of information in fisheries management: North Sea herring as an example. ICES Journal of Marine Science, 66, 22782283.CrossRefGoogle Scholar
Mardia, K. V., Kent, J. T., and Bibby, J. M. (1980). Multivariate Analysis. London: Academic Press.Google Scholar
Mariethoz, G., and Caers, J. (2015). Multiple-Point Geostatistics: Stochastic Modeling with Training Images. Hoboken, NJ: Wiley & Sons.Google Scholar
Mariethoz, G., Renard, P., and Straubhaar, J. (2010). The direct sampling method to perform multiple point geostatistical simulations. Water Resources Research, 46, W11536.CrossRefGoogle Scholar
Marin, J. M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012). Approximate Bayesian computational methods. Statistics and Computing, 22, 11671180.CrossRefGoogle Scholar
Martinelli, G., and Eidsvik, J. (2014). Dynamic exploration designs for graphical models using clustering with applications to petroleum exploration. Knosys, 58, 113126.Google Scholar
Martinelli, G., Eidsvik, J., and Hauge, R. (2013a). Dynamic decision making for graphical models applied to oil exploration. European Journal of Operational Research, 230, 688702.CrossRefGoogle Scholar
Martinelli, G., Eidsvik, J., Hauge, R., and Førland, M. D. (2011). Bayesian networks for prospect analysis in the North Sea. AAPG Bulletin, 95, 14231442.CrossRefGoogle Scholar
Martinelli, G., Eidsvik, J., Hokstad, K., and Hauge, R. (2014). Strategies for petroleum exploration based on Bayesian networks: a case study. Society of Petroleum Engineers Journal, 19, 564575.Google Scholar
Martinelli, G., Eidsvik, J., Sinding-Larsen, R., Rekstad, S., and Mukerji, T. (2013b). Building Bayesian networks from basin modeling scenarios for improved geological decision making. Petroleum Geoscience, 19, 289304.CrossRefGoogle Scholar
Matheson, J., and Howard, R. (1968). An introduction to decision analysis. InHoward, R. and Matheson, J., eds. The Principles and Applications of Decision Analysis, Vol. I. Strategic Decisions Group, pp. 17–55.Google Scholar
Matheson, J. E. (1990). Using influence diagrams to value information and control. In Oliver, R. M., Smith, J. Q., eds., Influence Diagrams, Belief Nets and Decision Analysis. Wiley & Sons, pp. 25–48.Google Scholar
Mavko, G., Mukerji, T., and Dvorkin, J. (2009). The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media, 2nd edn. Cambridge University Press.CrossRefGoogle Scholar
McCullagh, P., and Nelder, J. A. (1989). Generalized Linear Models. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
McLachlan, G., and Krishnan, T. (2008). The EM Algorithm and Extensions. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Meinshausen, N., and Buhlmann, P. (2006). High-dimensional graphs with the lasso. Annals of Statistics, 34, 14361462.CrossRefGoogle Scholar
Merkhofer, M. W. (1977). The value of information given decision flexibility. Management Science, 23, 716727.CrossRefGoogle Scholar
Meza, F. J., Hansen, J. W., and Osgood, D. (2008). Economic value of seasonal climate forecasts for agriculture: review of ex-ante assessments and recommendations for future research. Journal of Applied Meteorology and Climatology, 47, 12691286.CrossRefGoogle Scholar
Miles, R. (2007). The emergence of decision analysis. In Edwards, W., Miles, R., and von Winterfeldt, D., eds., Advances in Decision Analysis: From Foundations to Applications. Cambridge University Press, pp. 1331.CrossRefGoogle Scholar
Miller, A. C. (1975). The value of sequential information. Management Science, 22, 111.CrossRefGoogle Scholar
Mukerji, T., Avseth, P., Mavko, G., Takahashi, I., and Gonzalez, E. (2001). Statistical rock physics: combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization. The Leading Edge, 20, 313319.CrossRefGoogle Scholar
Muller, W. (2007). Collecting Spatial Data. Berlin: Springer-Verlag.Google Scholar
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press.Google Scholar
Newendorp, P. D., and Schuyler, J. R. (2013). Decision Analysis for Petroleum Exploration, 3rd edn. Aurora, CO: Planning Press.Google Scholar
Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46, 323351.CrossRefGoogle Scholar
Nowak, W., Rubin, Y., and de Barros, F. P. J. (2012). A hypothesis-driven approach to optimize field campaigns. Water Resources Research, 48, W06509.CrossRefGoogle Scholar
Panchal, J. H., Paredis, C. J. J., Allen, J. K., and Mistree, F. (2009). Managing design-process complexity: a value-of-information based approach for scale and decision decoupling. Journal of Computing and Information Science in Engineering, 9, 021005.CrossRefGoogle Scholar
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Francisco, CA: Morgan Kaufmann.Google Scholar
Peyrard, N., Sabbadin, R., Spring, D., Brook, B., and MacNally, R. (2013). Model-based adaptive spatial sampling for occurrence map construction. Statistics and Computing, 23, 2942.CrossRefGoogle Scholar
Phillips, J., Newman, A. M., and Walls, M. R. (2009). Utilizing a value of information framework to inprove ore collection and classification procedures. The Engineering Economist, 54, 5074.CrossRefGoogle Scholar
Pinto, J. R., de Agular, J. C., and Moraes, F. S. (2011). The value of information from time-lapse seismic data. The Leading Edge, 30, 572576.CrossRefGoogle Scholar
Polasky, R., and Solow, A. R. (2001). The value of information in reserve site selection. Biodiversity and Conservation, 10, 10511058.CrossRefGoogle Scholar
Powell, W. B. (2011). Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2nd edn. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Puterman, M. L. (2005). Markov Decision Processes: Discrete Stochastic Dynamic Programming. Hoboken, NJ: Wiley.Google Scholar
Pyrcz, M. J., and Deutsch, C. V. (2014). Geostatistical Reservoir Modeling. Oxford: Oxford University Press.Google Scholar
Raiffa, H. (1968). Decision Analysis: Introductory Lectures on Choices under Uncertainty. Boston: Addison-Wesley.Google Scholar
Rasmussen, C. E., and Williams, C. (2006). Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press.Google Scholar
Reeves, R., and Pettitt, A. N. (2004). Efficient recursions for general factorisable models. Biometrika, 91, 751757.CrossRefGoogle Scholar
Reich, B. J., and Fuentes, M. (2007). A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields. Annals of Applied Statistics, 1, 249264.CrossRefGoogle Scholar
Remy, N., Boucher, A., and Wu, J. (2008). Applied Geostatistics with SGeMS. Cambridge University Press.Google Scholar
Rezaie, J., Eidsvik, J., and Mukerji, T. (2014). Value of information analysis and Bayesian inversion for closed skew-normal distributions: applications to seismic amplitude variation with offset data. Geophysics, 79, R151R163.CrossRefGoogle Scholar
Rimstad, K., Avseth, P., and Omre, H. (2012). Hierarchical Bayesian lithology/fluid prediction: a North Sea case study. Geophysics, 77, B69B85.CrossRefGoogle Scholar
Rivoirard, J. (1987). Two key parameters when choosing the Kriging neighborhood. Mathematical Geology, 19, 851856.CrossRefGoogle Scholar
Royle, J. A. (2002). Exchange algorithm for constructing large spatial designs. Journal of Statistical Planning and Inference, 100, 121134.CrossRefGoogle Scholar
Royle, J. A., and Nychka, D. (1998). An algorithm for the construction of spatial coverage designs with implementation in SPLUS. Computers & Geosciences, 24, 479488.CrossRefGoogle Scholar
Rubinstein, R. Y., and Kroese, D. P. (2007). Simulation and the Monte Carlo Method. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Rue, H., and Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Rue, H., Martino, S., and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations. Journal of the Royal Statistical Society, B, 71, 319392.CrossRefGoogle Scholar
Sagan, C. (1994). Pale Blue Dot: A Vision of the Human Future in Space. New York: Random House.Google Scholar
Santner, T. J., Williams, B. J., and Notz, W. I. (2003). Design and Analysis of Computer Experiments. New York: Springer-Verlag.CrossRefGoogle Scholar
Schabenberger, O., and Gotway, C. A. (2009). Statistical Methods for Spatial Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Scheidt, C., and Caers, J. (2009). Uncertainty quantification in reservoir performance using distances and kernel methods – application to a West Africa deepwater turbidite reservoir. Society of Petroleum Engineers Journal, 14, 680692.Google Scholar
Schlaiffer, R. (1959). Probability and Statistics for Business Decisions. New York: McGraw-Hill.Google Scholar
Schon, J. H. (2011). Physical Properties of Rocks: A Workbook. Elsevier.Google Scholar
Scott, S. L. (2002). Bayesian methods for hidden Markov models: recursive computing in the 21st century. Journal of the American Statistical Association, 97, 337351.CrossRefGoogle Scholar
Shachter, R. (1986). Evaluating influence diagrams. Operations Research, 34, 871882.CrossRefGoogle Scholar
Shachter, R. (1988). Probabilistic inference and influence diagrams. Operations Research, 36, 589605.CrossRefGoogle Scholar
Shachter, R. (1999). Efficient value of information computation. In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann, pp. 594–601.Google Scholar
Shachter, R. (2007). Model building with belief networks and influence diagrams. In Edwards, W., Miles, R., and von Winterfeldt, D., eds., Advances in Decision Analysis: From Foundations to Applications. Cambridge University Press, pp. 177201.CrossRefGoogle Scholar
Shachter, R., and Peot, M. (1992). Decision making using probabilistic inference methods. In Dubois, D., Wellman, M. P., D’Ambrosio, B., and Smets, P., eds., Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann, pp. 276283.Google Scholar
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379324, reprinted in Shannon, C. E. and Weaver, W., The Mathematical Theory of Communication, University of Illinois Press, 1949, 1998.CrossRefGoogle Scholar
Shenoy, P. (1992). Valuation-based systems for Bayesian decision analysis. Operations Research, 40, 463484.CrossRefGoogle Scholar
Shenoy, P. (1998). Game trees for decision analysis. Theory and Decision, 44, 149171.CrossRefGoogle Scholar
Shewry, M. C., and Wynn, H. P. (1987). Maximum entropy sampling. Journal of Applied Statistics, 14, 165170.CrossRefGoogle Scholar
Silverman, B. W. (1988). Density Estimation for Statistics and Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Srivastava, R. M. (1994). An overview of stochastic methods for reservoir characterization. In Yarus, J. M. and Chambers, R. L., eds., Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies. American Association of Petroleum Geologists, pp. 316.Google Scholar
Stein, M. L. (1999). Statistical Interpolation of Spatial Data: Some Theory for Kriging. New York: Springer-Verlag.CrossRefGoogle Scholar
Stein, M. L., Chi, Z., and Welty, L. J. (2004). Approximating likelihood for large spatial data sets. Journal of the Royal Statistical Society, Series B, 66, 275296.CrossRefGoogle Scholar
Stien, M., and Kolbjørnsen, O. (2011). Facies modeling using a Markov mesh model specification. Mathematical Geosciences, 43, 611624.CrossRefGoogle Scholar
Strebelle, S. (2000). Sequential simulation drawing structures from training images. PhD thesis, Stanford University.Google Scholar
Strebelle, S. (2002). Conditional simulation of complex geological structures using multiple-point statistics. Mathematical Geology, 34, 1–21.CrossRefGoogle Scholar
Strebelle, S. B., and Journel, A. G. (2001). Reservoir modeling using multiple-point statistics. Society of Petroleum Engineers Journal, 71324.Google Scholar
Sucar, L. E., Morales, E. F., and Hoey, J. (2012). Decision Theory Models for Applications in Artificial Intelligence. Hershey, PA: IGI Global.Google Scholar
Sylta, Ø. (2004). Hydrocarbon migration modelling and exploration risk. PhD thesis, Norwegian University of Science and Technology (NTNU).Google Scholar
Sylta, Ø. (2008). Analysing exploration uncertainties by tight integration of seismic and hydrocarbon mifration modelling. Petroleum Geoscience, 14, 281289.CrossRefGoogle Scholar
Tahmasebi, P., Hezarkhani, A., and Sahimi, M. (2012). Multiple-point geostatistical modeling based on the cross-correlation function. Computational Geosciences, 16, 779797.CrossRefGoogle Scholar
Tatman, J., and Shachter, R. (1990). Dynamic programming and influence diagrams. IEEE Transactions on Systems, Man, and Cybernetics, 20, 365379.CrossRefGoogle Scholar
Taylor, H. M., and Karlin, S. (1994). An Introduction to Stochastic Modeling. London: Academic Press.Google Scholar
Tjelmeland, H., and Austad, H. M. (2012). Exact and approximate recursive calculations for binary Markov random fields defined on graphs. Journal of Graphical and Computational Statistics, 21, 758780.CrossRefGoogle Scholar
Tjelmeland, H., and Besag, J. (1996). Markov random fields with higher-order interactions. Scandinavian Journal of Statistics, 25, 415433.CrossRefGoogle Scholar
Trainor-Guitton, W. J., Caers, J., and Mukerji, T. (2011). A methodology for establishing a data reliability measure for value of spatial information problems. Mathematical Geosciences, 43, 929949.CrossRefGoogle Scholar
Trainor-Guitton, W. J. Hoversten, G. M., Ramirez, A., Roberts, J., Juliusson, E., Key, K., and Mellors, R. (2014). The value of spatial information for determining well placement: a geothermal example. Geophysics, 79, W27W41.CrossRefGoogle Scholar
Trainor-Guitton, W. J., Mukerji, T., and Knight, R. (2013). A methodology for quantifying the value of spatial information for dynamic Earth problems. Stochastic Environmental Research and Risk Assessment, 27, 969983.CrossRefGoogle Scholar
Tversky, A., and Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185, 11241131.CrossRefGoogle ScholarPubMed
Tviberg, S. (2011). To assess the petroleum net present value and accumulation process in a controlled Petromod environment. MSc thesis, Norwegian University of Science and Technology (NTNU).Google Scholar
Ulvmoen, M., Omre, H., and Buland, A. (2010). Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations: Part 2 – real case study, Geophysics, 75, B73B82.CrossRefGoogle Scholar
Vann, J., Jackson, S., and Bertoli, O. (2003). Quantitative Kriging neighborhood analysis for the mining geologist – a description of the method with worked case examples. In Proceedings of the Fifth International Mining Conference, The Australasian Institute of Mining and Metallurgy (The AusIMM), pp. 215–223.Google Scholar
van Wees, J., Mijnlie, H., Lutgert, J., Breunese, J., Bos, C., Rosenkranz, P., and Neele, F. (2008). A Bayesian belief network approach for assessing the impact of exploration prospect interdependency: an application to predict gas discoveries in the Netherlands. AAPG Bulletin, 92, 13151336.CrossRefGoogle Scholar
Varin, C., Reid, N., and Firth, D. (2011). An overview of composite likelihood methods. Statistica Sinica, 21, 542.Google Scholar
Vermeer, G. J. O. (2012). 3D Seismic Survey Design. Society of Exploration Geophysicists.Google Scholar
von Neumann, J., and Morgenstern, O. (1947). Theory of Games and Economic Behavior, 2nd edn. Princeton University Press.Google Scholar
von Winterfeldt, D., and Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge University Press.Google Scholar
Wackernagel, H. (2003). Multivariate Geostatistics, 3rd edn. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Wagner, J. M., Shamir, U., and Nemati, H. R. (1992). Groundwater quality management under uncertainty: stochastic programming approaches and the value of information. Water Resources Research, 28, 12331246.CrossRefGoogle Scholar
Welton, N. J., Ades, A. E., Caldwell, D. M., and Peters, T. J. (2008). Research prioritization based on expected value of partial perfect information: a case-study on interventions to increase uptake of breast cancer screening. Journal of the Royal Statistical Society, Series A, 171, 807841.CrossRefGoogle Scholar
Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. Hoboken, NJ: Wiley.Google Scholar
Wiles, L. J. (2004). Economics of weed management: principles and practices. Weed Technology, 18, 14031407.CrossRefGoogle Scholar
Willan, A. R., and Pinto, E. M. (2005). The value of information and optimal clinical trial design. Statistics in Medicine, 24, 17911806.CrossRefGoogle ScholarPubMed
Williams, B. K., Eaton, M. J., and Breininger, D. R. (2011). Adaptive resource management and the value of information. Ecological Modeling, 222, 34293436.CrossRefGoogle Scholar
Yokota, F., and Thompson, K. (2004a). Value of information literature analysis: a review of applications in health risk management. Medical Decision Making, 24, 287298.CrossRefGoogle ScholarPubMed
Yokota, F., and Thompson, K. (2004b). Value of information analysis in environmental health risk management decisions: past, present, and future. Risk Analysis, 24, 635650.CrossRefGoogle ScholarPubMed
Zan, K., and Bickel, J. E. (2013). Components of portfolio value of information. Decision Analysis, 10, 171183.CrossRefGoogle Scholar
Zetterlund, M., Norberg, T., Ericsson, L. O., and Rosen, L. (2011). Framework for value of information analysis in rock mass characterization for grouting purposes. Journal of Construction Engineering and Management, 137, 486497.CrossRefGoogle Scholar
Zhang, T., Switzer, P., and Journel, A. (2006). Filter-based classification of training image patterns for spatial simulation. Mathematical Geology, 38, 6380.CrossRefGoogle Scholar
Zimmerman, D. L. (2006). Optimal network design for spatial prediction, covariance estimation and empirical prediction. Environmetrics, 17, 635652.CrossRefGoogle Scholar

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  • References
  • Jo Eidsvik, Norwegian University of Science and Technology, Trondheim, Tapan Mukerji, Stanford University, California, Debarun Bhattacharjya, IBM T. J. Watson Research Center, New York
  • Book: Value of Information in the Earth Sciences
  • Online publication: 05 November 2015
  • Chapter DOI: https://doi.org/10.1017/CBO9781139628785.010
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  • References
  • Jo Eidsvik, Norwegian University of Science and Technology, Trondheim, Tapan Mukerji, Stanford University, California, Debarun Bhattacharjya, IBM T. J. Watson Research Center, New York
  • Book: Value of Information in the Earth Sciences
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  • References
  • Jo Eidsvik, Norwegian University of Science and Technology, Trondheim, Tapan Mukerji, Stanford University, California, Debarun Bhattacharjya, IBM T. J. Watson Research Center, New York
  • Book: Value of Information in the Earth Sciences
  • Online publication: 05 November 2015
  • Chapter DOI: https://doi.org/10.1017/CBO9781139628785.010
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