Statistical Decision Theory and its Application to Forest Engineering
Statistical decision theory and Bayesian statistics may be applied to the analysis of forest engineering problems as demonstrated in a hypothetical logging problem. If proper psychological assumptions exist on the part of the decision maker, "expected value" is the proper decision rule to select among alternative strategies. More realistically, he does not have a "linear" attitude toward money in a risk situation. Under these conditions, the "expected utility" decision rule would replace that of "expected value." Bayes' theorem allows forest engineers to adjust a priori outcome probabilities to reflect the effect of additional information. By incorporating the Bayesian aproach to probability with statistical decision theory, forest engineers may determine whether additional information is desirable.
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Document Type: Journal Article
Affiliations: Associate Professor of Forestry, Oregon State University, Corvallis, Currently on Leave as a Ford Foundation Fellow at Indiana University, Bloomington
Publication date: 1965-04-01
2016 Impact Factor: 1.675 (Rank 20/64 in forestry)
Average time from submission to first decision: 39.6 days*
June 1, 2016 to Feb. 28, 2017
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