Statistical Decision Theory and its Application to Forest Engineering

Author: Dane, C. W.

Source: Journal of Forestry, Volume 63, Number 4, 1 April 1965 , pp. 276-279(4)

Publisher: Society of American Foresters

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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.

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: April 1, 1965

More about this publication?
  • The Journal of Forestry is the most widely circulated scholarly forestry journal in the world. In print since 1902, the Journal has received several national awards for excellence. The mission of the Journal of Forestry is to advance the profession of forestry by keeping forest management professionals informed about significant developments and ideas in the many facets of forestry: economics, education and communication, entomology and pathology, fire, forest ecology, geospatial technologies, history, international forestry, measurements, policy, recreation, silviculture, social sciences, soils and hydrology, urban and community forestry, utilization and engineering, and wildlife management. The Journal is published bimonthly: January, March, May, July, September, and November.
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