UID:
almafu_9960117371002883
Umfang:
1 online resource (xiii, 385 pages) :
,
digital, PDF file(s).
ISBN:
1-316-42999-7
,
1-316-43425-7
,
1-316-43496-6
,
1-316-43780-9
,
1-316-43567-9
,
1-316-43922-4
,
1-139-62878-X
Inhalt:
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.
Anmerkung:
Title from publisher's bibliographic system (viewed on 11 Nov 2015).
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Cover; Half-title; Title page; Copyright information; Table of contents; Preface; Acknowledgments; 1 Introduction; 1.1 What is the value of information?; 1.2 Motivating examples from the Earth sciences; 1.3 Contributions of this book; 1.4 Organization; 1.5 Intended audience and prerequisites; 1.6 Bibliographic notes; 2 Statistical models and methods; 2.1 Uncertainty quantification, information gathering, and data examples; 2.2 Notation and probability models; 2.2.1 Univariate probability distributions; 2.2.2 Multivariate probability distributions
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2.3 Conditional probability, graphical models, and Bayes' rule2.3.1 Conditional probability; 2.3.2 Graphical models; 2.3.3 Bayesian updating from data; 2.3.4 Examples; Treasure Island: The pirate example; Gotta get myself connected: Bayesian network example; Never break the chain: Markov chain example; For whom the bell tolls: Gaussian projects example; 2.4 Inference of model parameters; 2.4.1 Maximum likelihood estimation; 2.4.2 Examples; I love rock and ore: mining oxide grade example; Never break the chain: Markov chain example; 2.5 Monte Carlo methods and other approximation techniques
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2.5.1 Analysis by simulation2.5.2 Solving integrals; 2.5.3 Sampling methods; 2.5.4 Example; Risky business: petroleum prospect risking example; 2.6 Bibliographic notes; Models; Estimation and sampling; 3 Decision analysis; 3.1 Background; 3.2 Decision situations: terminology and notation; 3.2.1 Decisions, uncertainties, and values; 3.2.2 Utilities and certain equivalent; 3.2.3 Maximizing expected utility; 3.2.4 Examples; Treasure island: the pirate example; For whom the bell tolls: Gaussian projects example; 3.3 Graphical models; 3.3.1 Decision trees; 3.3.2 Influence diagrams; 3.3.3 Examples
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For whom the bell tolls: Gaussian projects exampleMacKenna's gold: oil and gold example; Time after time: time-lapse seismic example; Value from 4-D seismic monitoring; Influence diagrams for 4-D seismic monitoring; Observable property nodes; Reservoir property nodes; Seismic property nodes; 3.4 Value of information; 3.4.1 Definition; 3.4.2 Perfect versus imperfect information; 3.4.3 Relevant, material, and economic information; 3.4.4 Examples; Treasure island: the pirate example; For whom the bell tolls: Gaussian projects example; 3.5 Bibliographic notes; Decision analysis fundamentals
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Graphical modelsVOI fundamentals; VOI for canonical problems; Computational issues and application reviews; 4 Spatial modeling; 4.1 Goals of stochastic modeling of spatial processes; 4.2 Random fields, variograms, and covariance; 4.3 Prediction and simulation; 4.3.1 Spatial prediction and Kriging; 4.3.2 Common geostatistical stochastic simulation methods; 4.4 Gaussian models; 4.4.1 The spatial regression model; 4.4.2 Optimal spatial prediction: Kriging; 4.4.3 Multivariate hierarchical spatial regression model; 4.4.4 Examples; Norwegian wood: forestry example
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I love rock and ore: mining oxide grade example
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English
Weitere Ausg.:
ISBN 1-107-04026-4
Sprache:
Englisch
Schlagwort(e):
Electronic books
URL:
https://doi.org/10.1017/CBO9781139628785
URL:
https://doi.org/10.1017/CBO9781139628785
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