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dc.contributor.advisorBickel, J. Ericen
dc.creatorRamakrishnan, Arjunen
dc.date.accessioned2011-01-03T17:53:50Zen
dc.date.accessioned2011-01-03T17:53:57Zen
dc.date.available2011-01-03T17:53:50Zen
dc.date.available2011-01-03T17:53:57Zen
dc.date.created2010-08en
dc.date.issued2011-01-03en
dc.date.submittedAugust 2010en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-08-1735en
dc.descriptiontexten
dc.description.abstractValue of information is one of the key features of decision analysis. This work deals with providing a consistent and functional methodology to determine VOI on proposed well tests in the presence of uncertainties. This method strives to show that VOI analysis with the help of discretized versions of continuous probability distributions with conventional decision trees can be very accurate if the optimal method of discrete approximation is chosen rather than opting for methods such as Monte Carlo simulation to determine the VOI. This need not necessarily mean loss of accuracy at the cost of simplifying probability calculations. Both the prior and posterior probability distributions are assumed to be continuous and are discretized to find the VOI. This results in two steps of discretizations in the decision tree. Another interesting feature is that there lies a level of decision making between the two discrete approximations in the decision tree. This sets it apart from conventional discretized models since the accuracy in this case does not follow the rules and conventions that normal discrete models follow because of the decision between the two discrete approximations. The initial part of the work deals with varying the number of points chosen in the discrete model to test their accuracy against different correlation coefficients between the information and the actual values. The latter part deals more with comparing different methods of existing discretization methods and establishing conditions under which each is optimal. The problem is comprehensively dealt with in the cases of both a risk neutral and a risk averse decision maker.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectDiscretizationen
dc.subjectValue of informationen
dc.subjectDiscrete approximationen
dc.subjectDecision analysisen
dc.subjectBayesian inferenceen
dc.titleValue of information and the accuracy of discrete approximationsen
dc.date.updated2011-01-03T17:53:57Zen
dc.contributor.committeeMemberLake, Larry W.en
dc.description.departmentMechanical Engineeringen
dc.type.genrethesisen
thesis.degree.departmentMechanical Engineeringen
thesis.degree.disciplineOperations Research and Industrial Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen


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