Approximating Joint Probability Distributions Given Partial Information

Luis V. Montiel () and J. Eric Bickel ()
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Luis V. Montiel: Graduate Program in Operations Research and Industrial Engineering, University of Texas at Austin, Austin, Texas 78712
J. Eric Bickel: Graduate Program in Operations Research and Industrial Engineering, University of Texas at Austin, Austin, Texas 78712

Decision Analysis, 2013, vol. 10, issue 1, 26-41

Abstract: In this paper, we propose new methods to approximate probability distributions that are incompletely specified. We compare these methods to the use of maximum entropy and quantify the accuracy of all methods within the context of an illustrative example. We show that within the context of our example, the methods we propose are more accurate than existing methods.

Keywords: maximum entropy; incomplete information; probabilistic dependence; analytic center; stochastic optimization; dynamic programming; sequential exploration; practice (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:10:y:2013:i:1:p:26-41

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