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  • Cited by 60
Publisher:
Cambridge University Press
Online publication date:
November 2015
Print publication year:
2015
Online ISBN:
9781139628785

Book description

Gathering the right kind and the right amount of information is crucial for any decision-making process. This book presents a unified framework for assessing the value of potential data gathering schemes by integrating spatial modelling and decision analysis, with a focus on the Earth sciences. The authors discuss the value of imperfect versus perfect information, and the value of total versus partial information, where only subsets of the data are acquired. Concepts are illustrated using a suite of quantitative tools from decision analysis, such as decision trees and influence diagrams, as well as models for continuous and discrete dependent spatial variables, including Bayesian networks, Markov random fields, Gaussian processes, and multiple-point geostatistics. Unique in scope, this book is of interest to students, researchers and industry professionals in the Earth and environmental sciences, who use applied statistics and decision analysis techniques, and particularly to those working in petroleum, mining, and environmental geoscience.

Reviews

'The book’s authors have made a mindful attempt at teaching a relatively complicated topic, and one of growing importance, to a non-expert audience composed of geoscientists. … This book is written while keeping researchers, graduate students, and practitioners in earth-science-related industries in mind. Hence, the mathematical rigor is kept at a level that is appropriate for such an audience, without too much emphasis on details. It is a well-organized volume that uses a number of numerical examples to illustrate the applicability of VOI analysis to earth-science problems. Additional data sets and Matlab codes are available as online supplementary material. Hence, it is well suited for self-learning. … I strongly recommend it to anyone using or researching the application of decision analysis techniques like VOI analysis in managing earth resources.'

Amit Padhi Source: The Leading Edge

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Contents

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.
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.
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.
Allard, D., and Naveau, P. (2007). Simulating and analyzing spatial skew normal random fields. Communications in Statistics, 36, 18211834.
Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis. New York: Wiley.
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.
Arpat, B., and Caers, J. (2007). Conditional simulations with patterns. Mathematical Geology, 39, 177203.
Ash, R. B. (1965). Information Theory. New York: Dover Publications.
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.
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.
Avseth, P., Mukerji, T., and Mavko, G. (2005). Quantitative Seismic Interpretation. Cambridge University Press.
Bachrach, R. (2006). Joint estimation of porosity and saturation using stochastic rock-physics modelling. Geophysics, 71, O53O63.
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.
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.
Banerjee, S., Gelfand, A., and Carlin, B. (2004). Hierachical Modeling and Analysis for Spatial Data. Boca Raton, FL: Chapman & Hall/CRC Press.
Bardossy, A., and Li, J. (2008). Geostatistical interpolation using copulas. Water Resources Research, 44, W07412.
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.
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.
Beaumont, M., Zhang, W., and Balding, D. (2002). Approximate Bayesian computation in population genetics. Genetics, 162, 20252032.
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.
Bertsekas, D. P. (2012). Dynamic Programming and Optimal Control. Vol. II of Approximate Dynamic Programming, 4th edn. Athena Scientific.
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B, 36, 192236.
Besag, J. (1986). On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B, 48, 259302.
Bhattacharjya, D., and Deleris, L. (2014). The value of information in some variations of the stopping problem. Decision Analysis, 11, 189203.
Bhattacharjya, D., Eidsvik, J., and Mukerji, T. (2010). The value of information in spatial decision making. Mathematical Geosciences, 42, 141163.
Bhattacharjya, D., Eidsvik, J., and Mukerji, T. (2013). The value of information in portfolio problems with dependent projects. Decision Analysis, 10, 341351.
Bhattacharjya, D., and Mukerji, T. (2006). Using influence diagrams to analyze decisions in 4D seismic reservoir monitoring. The Leading Edge, 25, 12361239.
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.
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.
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.
Bickel, J. E. (2008). The relationship between perfect and imperfect information in a two-action risk-sensitive problem. Decision Analysis, 5, 116128.
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.
Bickel, J. E., and Smith, J. E. (2006). Optimal sequential exploration: a binary learning model. Decision Analysis, 3, 1632.
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.
Bielza, C., Gomez, M., and Shenoy, P. (2010). Modeling challenges with influence diagrams: constructing probability and utility models. Decision Support Systems, 49, 354364.
Bielza, C., and Shenoy, P. (1999). A comparison of graphical techniques for asymmetric decision problems. Management Science, 45, 15521569.
Blackwell, D. (1953). Equivalent comparisons of experiments. Annals of Mathematical Statistics, 24, 265272.
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.
Borg, I., and Groenen, P. J. F. (2005). Modern Multidimensional Scaling: Theory and Applications, 2nd edn. New York: Springer-Verlag.
Borgault, G. (1997). Using non-Gaussian distributions in geostatistical simulation. Mathematical Geology, 29, 315334.
Borgos, H. G., Omre, H., and Townsend, C. (2002). Size distribution of geological faults: model choice and parameter estimation. Statistical Modeling, 2, 217234.
Borisova, T., Shortle, J., Horan, R. D., and Abler, D. (2005). Value of information for water quality management. Water Resources Research, 41, W06004.
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.
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.
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.
Bratvold, R. B., and Begg, S. H. (2010). Making Good Decisions. Richardson, TX: Society of Petroleum Engineers.
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.
Breslow, N. E., and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 925.
Brockwell, P. J., and Davis, R. A. (2009). Time Series: Theory and Methods. New York: Springer-Verlag.
Brown, D. B and Smith, J. E. (2013). Optimal sequential exploration: bandits, clairvoyants, and wildcats. Operations Research, 60, 262274.
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.
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.
Cabrera, V. E., Letson, D., and Podesta, G. (2007). The value of climate information when farm programs matter. Agricultural Systems, 93, 2542.
Caers, J. (2005). Petroleum Geostatistics. Richardson, TX: Society of Petroleum Engineers.
Caers, J. (2011). Modeling Uncertainty in the Earth Sciences. Chichester, UK: Wiley-Blackwell.
Carlin, B. P., and Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.
Chan, G., and Wood, A. T. A. (1997). An algorithm for simulating stationary Gaussian random fields. Applied Statistics, 46, 171181.
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.
Chiles, J. P., and Delfiner, P. (2012). Geostatistics: Modeling Spatial Uncertainty, 2nd edn. New York: Wiley.
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.
Clemen, R., and Reilly, T. (1999). Making Hard Decisions with Decision Tools Suite. Pacific Grove, CA: Duxbury Press.
Clemen, R., and Winkler, R. (1985). Limits for the precision and value of information from dependent sources. Operations Research, 33, 427442.
Covaliu, Z., and Oliver, R. M. (1995). Representation and solution of decision problems using sequential decision diagrams. Management Science, 41, 18601881.
Cover, T., and Thomas, J. (2006). Elements of Information Theory, 2nd edn. New York: Wiley-Interscience.
Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J. (2007). Probabilistic Networks and Expert Systems. New York: Springer-Verlag.
Cox, T. F., and Cox, M. A. A. (2001). Multidimensional Scaling, 2nd edn. Boca Raton, FL: Chapman and Hall/CRC.
Cressie, N. (1993). Statistics for Spatial Data. New York: Wiley.
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.
Cressie, N., and Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. New York: Wiley.
Curtis, A. (2004). Theory of model-based geophysical survey and experimental design: Part A – Linear problems. The Leading Edge, 23, 9971004.
Daly, C., and Caers, J. (2010). Multi-point geostatistics – An introductory overview. First Break, 28, 3947.
Darwiche, A. (2009). Modeling and Reasoning with Bayesian Networks. Cambridge University Press.
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.
Demidenko, E. (2004). Mixed Models: Theory and Applications. New York: Wiley.
Deutsch, C., and Journel, A. G. (1992). GSLIB: Geostatistical Software Library and User’s Guide. New York: Oxford University Press.
Diggle, P., and Lophaven, S. (2006). Bayesian geostatistical design. Scandinavian Journal of Statistics, 33, 5364.
Diggle, P. J., Tawn, J. A., and Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society, Series C, 47, 299350.
Dobbie, M. J., Henderson, B. L., and Stevens, D. L. (2008). Sparse sampling: spatial design for monitoring stream networks. Statistics Surveys, 2, 113153.
Dobson, A. J., and Barnett, A. (2008). An Introduction to Generalized Linear Models. Boca Raton, FL: Chapman & Hall/CRC Press.
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.
Doucet, A., de Freitas, N., and Gordon, N. (2001). Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag.
Dvorkin, J., and Nur, A. (1996). Elasticity of high-porosity sandstones: theory for two North Sea datasets. Geophysics, 61, 13631370.
Dyer, J. S., and Sarin, R. (1982). Relative risk aversion. Management Science, 28, 875886.
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.
Efron, B., and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC Press.
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.
Eidsvik, J., Avseth, P., Omre, H., Mukerji, T., and Mavko, G. (2004a). Stochastic reservoir characterization using prestack seismic data. Geophysics, 69, 978993.
Eidsvik, J., Bhattacharjya, D., and Mukerji, T. (2008). Value of information of seismic amplitude and CSEM resistivity. Geophysics, 73, R59R69.
Eidsvik, J., and Ellefmo, S. L. (2013). The value of information in mineral exploration within a multi-Gaussian framework. Mathematical Geosciences, 45, 777798.
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.
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.
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).
Evangelou, E., and Zhu, Z. (2012). Optimal predictive design augmentation for spatial generalised linear mixed models. Journal of Statistical Planning and Inference, 142, 32423253.
Evensen, G. (2009). Data Assimilation: The Ensemble Kalman Filter. Berlin: Springer-Verlag.
Farquhar, P. (1984). State of the art – utility assessment methods. Management Science, 30, 12831300.
Ferreira, M. A. R., and Lee, H. K. H. (2007). Multiscale Modeling: A Bayesian Perspective. New York: Springer-Verlag.
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.
Frazier, P. I., and Powell, W. B. (2010). Paradoxes in learning and the marginal value of information. Decision Analysis, 7, 378403.
Friedman, J., Hastie, T., and Tibshirani, R. (2009). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432441.
Froidevaux, R. (1992). Probability field simulation. In A. Soares, ed., Geostatistics Tróia, Proceedings of the 4th International Geostatistical Congress, Kluwer Academic Publishers.
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.
Fuentes, M., Chaudhuri, A., and Holland, D. M. (2007). Bayesian entropy for spatial sampling design of environmental data. Environmental and Ecological Statistics, 14, 323340.
Gaetan, C., and Guyon, X. (2010). Spatial Statistics and Modelling. New York: Springer-Verlag.
Gamerman, D., and Lopes, H. F. (2006). Markov Chain Monte Carlo. Boca Raton, FL: Chapman & Hall/CRC Press.
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.
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.
Genz, A., and Bretz, F. (2009). Computation of Multivariate Normal and T Probabilities: Lecture Notes in Statistics. Berlin: Springer-Verlag.
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.
Gneiting, T., and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102, 359378.
Golub, G. H., and van Loan, C. F. (1996). Matrix Computations. Baltimore, MD: John Hopkins University Press.
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.
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. New York: Oxford University Press.
Gramacy, R. B., and Apley, D. W. (2015). Local Gaussian process approximation for large computer experiments. Journal of Computational and Graphical Statistics.
Gray, R. M. (2006). Toeplitz and circulant matrices: a review. Foundations and Trends in Communication and Information Theory, 2, 155239.
Grayson, C. J., Jr. (1960). Decisions Under Uncertainty: Drilling Decisions by Oil and Gas Operators. Cambridge, MA: Harvard University Press.
Green, P. J., Hjort, N. L., and Richardson, S. (eds.) (2003). Highly Structured Stochastic Systems. New York: Oxford University Press.
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.
Hansen, G. J. A., and Jones, M. L. (2008). The value of information in fishery management. Fisheries, 33, 340348.
Hantschel, T., and Kauerauf, A. I. (2009). Fundamentals of Basin and Petroleum Systems Modelling. Berlin: Springer-Verlag.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning. New York: Springer-Verlag.
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.
Heckerman, D., and Shachter, R. (1995). Decision-theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research, 3, 405430.
Hilton, R. (1981). The determinants of information value: synthesizing some general results. Management Science, 27, 5764.
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.
Houck, R. T. (2007). Time-lapse seismic repeatability – how much is enough? The Leading Edge, 26, 828834.
Houck, R. T., and Pavlov, D. A. (2006). Evaluating reconnaissance CSEM survey designs using detection theory. The Leading Edge, 25, 9941004.
Howard, R. (1964). Decision analysis: applied decision theory. In Proceedings of the 4th International Conference on Operational Research, Wiley-Interscience, pp. 55–71.
Howard, R. (1966). Information value theory. IEEE Transactions on Systems Science and Cybernetics, 2, 2226.
Howard, R. (1967). Value of information lotteries. IEEE Transactions on Systems Science and Cybernetics, 3, 5460.
Howard, R. (1971). Proximal decision analysis. Management Science, 17, 507541.
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.
Howard, R. A., and Abbas, A. (2015). Foundations of Decision Analysis. London: Pearson Education.
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.
Illian, J., Penttinen, A., Stoyan, H., and Stoyan, D. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. Chichester, UK: Wiley.
Isaaks, E. H., and Srivastava, R. M. (1989). An Introduction to Applied Geostatistics. Oxford: Oxford University Press.
Izenman, A. J. (2008). Modern Multivariate Statistical Techniques. New York: Springer-Verlag.
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.
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.
Jensen, F. V. (1996). An Introduction to Bayesian Networks. London: UCL Press.
Jensen, F. V., and Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs, 2nd edn. New York: Springer-Verlag.
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.
Joe, H. (2014). Dependence Modeling with Copulas. Boca Raton, FL: Chapman & Hall/CRC Press.
Johnson, N. L., Kotz, S., and Balakrishnan, N. (1994). Continuous Univariate Distributions. Hoboken, NJ: Wiley.
Johnson, N. L., Kotz, S., and Balakrishnan, N. (1997). Discrete Multivariate Distributions. Hoboken, NJ: Wiley.
Johnson, R. A., and Wichern, D. W. (2012). Applied Multivariate Statistical Analysis. Delhi: Phi Learning Private Limited.
Journel, A. G. (1983). Nonparametric estimation of spatial distributions. Mathematical Geology, 15, 445468.
Journel, A. G., and Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press. Reprint by Blackburn Press, Caldwell, NJ, 2004.
Kangas, A. S. (2010). The value of forest information. European Journal of Forest Research, 129, 863874.
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.
Kaufman, G. (1993). Statistical issues in the assessment of undiscovered oil and gas resources. The Energy Journal, 14, 183215.
Kazianka, H., and Pilz, J. (2011). Bayesian spatial modeling and interpolation using copulas. Computers & Geosciences, 37, 310319.
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.
Keeney, R., and Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Hoboken, NJ: Wiley.
Keisler, J. M. (2004). Value of information in portfolio decision analysis. Decision Analysis, 1, 177189.
Keisler, J. M. (2005). Additivity of information in two-act linear loss decisions with normal priors. Risk Analysis, 25, 351359.
Keisler, J. M., and Brodfuehrer, M. (2009). An application of value-of-information to decision process reengineering. Engineering Economist, 54, 197221.
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.
Kelkar, M., and Perez, G. (2002). Applied Geostatistics for Reservoir Characterization. Richardson, TX: Society of Petroleum Engineers.
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.
Kim, H. M., and Mallick, B. K. (2004). A Bayesian prediction using the skew Gaussian distribution. Journal of Statistical Planning and Inference, 120, 85101.
Kirkwood, C. (1993). An algebraic approach to formulating and solving large models for sequential decisions under uncertainty. Management Science, 39, 900913.
Kirkwood, C. (2004). Approximating risk aversion in decision analysis applications. Decision Analysis, 1, 5167.
Kolbjørnsen, O., Hauge, R., Drange-Espeland, M., and Buland, A. (2012). Model-based fluid factor for controlled source electromagnetic data. Geophysics, 77, E21E31.
Koller, D., and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: MIT Press.
Konishi, C. (2014). Evaluation of uncertainty and risk of CO2 sequestration with stochastic models conditioned by seismic and well data. MSc thesis, Stanford University.
Kontoghiorghes, E. J. (ed.) (2005). Handbook of Parallel Computing and Statistics. Boca Raton, FL: Chapman & Hall/CRC Press.
Kotz, S., Johnson, N. L., and Balakrishnan, N. (2000). Continuous Multivariate Distributions. Hoboken, NJ: Wiley.
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.
Krause, A., and Guestrin, C. (2009). Optimal value of information in graphical models. Journal of Artificial Intelligence Research, 35, 557591.
Lantuejoul, C. (2002). Geostatistical Simulations: Models and Algorithms. Berlin: Springer-Verlag.
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.
Lawler, G. F. (2006). Introduction to Stochastic Processes. Boca Raton, FL: Chapman & Hall/CRC Press.
Le, N. D., and Zidek, J. V. (2006). Statistical Analysis of Environmental Space-Time Processes. New York: Springer-Verlag.
Lichtenstein, S., and Slovic, P. (2006). The Construction of Preference. Cambridge University Press.
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.
Lilleborge, M., Hauge, R., and Eidsvik, J. (in press). Information gathering in Bayesian networks applied to petroleum prospecting. Mathematical Geosciences.
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.
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.
Liu, J. L. (2001). Monte Carlo Strategies in Scientific Computing. New York: Springer-Verlag.
Lumley, D. (2001). Time-lapse seismic reservoir monitoring. Geophysics, 66, 5053.
Lumley, D., Behrens, R. A., and Wang, Z. (1997). Assessing the technical risk of a 4-D seismic project. The Leading Edge, 16, 12871292.
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.
MacDonald, I. L., and Zucchini, W. (1997). Hidden Markov and Other Models for Discrete-Valued Time Series. Boca Raton, FL: Chapman & Hall/CRC Press.
Manoharan, N. (2014). K-nearest neighbour methods for value of information in petroleum decision making. MSc thesis, Norwegian University of Science and Technology (NTNU).
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.
Mardia, K. V., Kent, J. T., and Bibby, J. M. (1980). Multivariate Analysis. London: Academic Press.
Mariethoz, G., and Caers, J. (2015). Multiple-Point Geostatistics: Stochastic Modeling with Training Images. Hoboken, NJ: Wiley & Sons.
Mariethoz, G., Renard, P., and Straubhaar, J. (2010). The direct sampling method to perform multiple point geostatistical simulations. Water Resources Research, 46, W11536.
Marin, J. M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012). Approximate Bayesian computational methods. Statistics and Computing, 22, 11671180.
Martinelli, G., and Eidsvik, J. (2014). Dynamic exploration designs for graphical models using clustering with applications to petroleum exploration. Knosys, 58, 113126.
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.
Martinelli, G., Eidsvik, J., Hauge, R., and Førland, M. D. (2011). Bayesian networks for prospect analysis in the North Sea. AAPG Bulletin, 95, 14231442.
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.
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.
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.
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.
Mavko, G., Mukerji, T., and Dvorkin, J. (2009). The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media, 2nd edn. Cambridge University Press.
McCullagh, P., and Nelder, J. A. (1989). Generalized Linear Models. Boca Raton, FL: Chapman & Hall/CRC Press.
McLachlan, G., and Krishnan, T. (2008). The EM Algorithm and Extensions. Hoboken, NJ: Wiley.
Meinshausen, N., and Buhlmann, P. (2006). High-dimensional graphs with the lasso. Annals of Statistics, 34, 14361462.
Merkhofer, M. W. (1977). The value of information given decision flexibility. Management Science, 23, 716727.
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.
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.
Miller, A. C. (1975). The value of sequential information. Management Science, 22, 111.
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.
Muller, W. (2007). Collecting Spatial Data. Berlin: Springer-Verlag.
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press.
Newendorp, P. D., and Schuyler, J. R. (2013). Decision Analysis for Petroleum Exploration, 3rd edn. Aurora, CO: Planning Press.
Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46, 323351.
Nowak, W., Rubin, Y., and de Barros, F. P. J. (2012). A hypothesis-driven approach to optimize field campaigns. Water Resources Research, 48, W06509.
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.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Francisco, CA: Morgan Kaufmann.
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.
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.
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.
Polasky, R., and Solow, A. R. (2001). The value of information in reserve site selection. Biodiversity and Conservation, 10, 10511058.
Powell, W. B. (2011). Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2nd edn. Hoboken, NJ: Wiley.
Puterman, M. L. (2005). Markov Decision Processes: Discrete Stochastic Dynamic Programming. Hoboken, NJ: Wiley.
Pyrcz, M. J., and Deutsch, C. V. (2014). Geostatistical Reservoir Modeling. Oxford: Oxford University Press.
Raiffa, H. (1968). Decision Analysis: Introductory Lectures on Choices under Uncertainty. Boston: Addison-Wesley.
Rasmussen, C. E., and Williams, C. (2006). Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press.
Reeves, R., and Pettitt, A. N. (2004). Efficient recursions for general factorisable models. Biometrika, 91, 751757.
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.
Remy, N., Boucher, A., and Wu, J. (2008). Applied Geostatistics with SGeMS. Cambridge University Press.
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.
Rimstad, K., Avseth, P., and Omre, H. (2012). Hierarchical Bayesian lithology/fluid prediction: a North Sea case study. Geophysics, 77, B69B85.
Rivoirard, J. (1987). Two key parameters when choosing the Kriging neighborhood. Mathematical Geology, 19, 851856.
Royle, J. A. (2002). Exchange algorithm for constructing large spatial designs. Journal of Statistical Planning and Inference, 100, 121134.
Royle, J. A., and Nychka, D. (1998). An algorithm for the construction of spatial coverage designs with implementation in SPLUS. Computers & Geosciences, 24, 479488.
Rubinstein, R. Y., and Kroese, D. P. (2007). Simulation and the Monte Carlo Method. Hoboken, NJ: Wiley.
Rue, H., and Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications. Boca Raton, FL: Chapman & Hall/CRC Press.
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.
Sagan, C. (1994). Pale Blue Dot: A Vision of the Human Future in Space. New York: Random House.
Santner, T. J., Williams, B. J., and Notz, W. I. (2003). Design and Analysis of Computer Experiments. New York: Springer-Verlag.
Schabenberger, O., and Gotway, C. A. (2009). Statistical Methods for Spatial Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.
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.
Schlaiffer, R. (1959). Probability and Statistics for Business Decisions. New York: McGraw-Hill.
Schon, J. H. (2011). Physical Properties of Rocks: A Workbook. Elsevier.
Scott, S. L. (2002). Bayesian methods for hidden Markov models: recursive computing in the 21st century. Journal of the American Statistical Association, 97, 337351.
Shachter, R. (1986). Evaluating influence diagrams. Operations Research, 34, 871882.
Shachter, R. (1988). Probabilistic inference and influence diagrams. Operations Research, 36, 589605.
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.
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.
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.
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.
Shenoy, P. (1992). Valuation-based systems for Bayesian decision analysis. Operations Research, 40, 463484.
Shenoy, P. (1998). Game trees for decision analysis. Theory and Decision, 44, 149171.
Shewry, M. C., and Wynn, H. P. (1987). Maximum entropy sampling. Journal of Applied Statistics, 14, 165170.
Silverman, B. W. (1988). Density Estimation for Statistics and Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.
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.
Stein, M. L. (1999). Statistical Interpolation of Spatial Data: Some Theory for Kriging. New York: Springer-Verlag.
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.
Stien, M., and Kolbjørnsen, O. (2011). Facies modeling using a Markov mesh model specification. Mathematical Geosciences, 43, 611624.
Strebelle, S. (2000). Sequential simulation drawing structures from training images. PhD thesis, Stanford University.
Strebelle, S. (2002). Conditional simulation of complex geological structures using multiple-point statistics. Mathematical Geology, 34, 1–21.
Strebelle, S. B., and Journel, A. G. (2001). Reservoir modeling using multiple-point statistics. Society of Petroleum Engineers Journal, 71324.
Sucar, L. E., Morales, E. F., and Hoey, J. (2012). Decision Theory Models for Applications in Artificial Intelligence. Hershey, PA: IGI Global.
Sylta, Ø. (2004). Hydrocarbon migration modelling and exploration risk. PhD thesis, Norwegian University of Science and Technology (NTNU).
Sylta, Ø. (2008). Analysing exploration uncertainties by tight integration of seismic and hydrocarbon mifration modelling. Petroleum Geoscience, 14, 281289.
Tahmasebi, P., Hezarkhani, A., and Sahimi, M. (2012). Multiple-point geostatistical modeling based on the cross-correlation function. Computational Geosciences, 16, 779797.
Tatman, J., and Shachter, R. (1990). Dynamic programming and influence diagrams. IEEE Transactions on Systems, Man, and Cybernetics, 20, 365379.
Taylor, H. M., and Karlin, S. (1994). An Introduction to Stochastic Modeling. London: Academic Press.
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.
Tjelmeland, H., and Besag, J. (1996). Markov random fields with higher-order interactions. Scandinavian Journal of Statistics, 25, 415433.
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.
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.
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.
Tversky, A., and Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185, 11241131.
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).
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.
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.
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.
Varin, C., Reid, N., and Firth, D. (2011). An overview of composite likelihood methods. Statistica Sinica, 21, 542.
Vermeer, G. J. O. (2012). 3D Seismic Survey Design. Society of Exploration Geophysicists.
von Neumann, J., and Morgenstern, O. (1947). Theory of Games and Economic Behavior, 2nd edn. Princeton University Press.
von Winterfeldt, D., and Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge University Press.
Wackernagel, H. (2003). Multivariate Geostatistics, 3rd edn. Berlin: Springer-Verlag.
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.
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.
Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. Hoboken, NJ: Wiley.
Wiles, L. J. (2004). Economics of weed management: principles and practices. Weed Technology, 18, 14031407.
Willan, A. R., and Pinto, E. M. (2005). The value of information and optimal clinical trial design. Statistics in Medicine, 24, 17911806.
Williams, B. K., Eaton, M. J., and Breininger, D. R. (2011). Adaptive resource management and the value of information. Ecological Modeling, 222, 34293436.
Yokota, F., and Thompson, K. (2004a). Value of information literature analysis: a review of applications in health risk management. Medical Decision Making, 24, 287298.
Yokota, F., and Thompson, K. (2004b). Value of information analysis in environmental health risk management decisions: past, present, and future. Risk Analysis, 24, 635650.
Zan, K., and Bickel, J. E. (2013). Components of portfolio value of information. Decision Analysis, 10, 171183.
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.
Zhang, T., Switzer, P., and Journel, A. (2006). Filter-based classification of training image patterns for spatial simulation. Mathematical Geology, 38, 6380.
Zimmerman, D. L. (2006). Optimal network design for spatial prediction, covariance estimation and empirical prediction. Environmetrics, 17, 635652.

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