Bayesian Analysis on Quantitative Decision-Making

Quantitative decision-making is thought of most often as an objective exercise based only on the cold analysis of verifiable hard facts. Intuition and even experience tends to be excluded from quantitative decision-making on the grounds that such information is subjective in character, and, thus, has no role in quantitative analysis. Quantitative decision-making is based in large part on the ability of decision-makers to make inferences about the probabilities of occurrence of future events from the analyses of objective data (Markowitz and Xu 60-69). One means of improving probability estimates in such predictions, however, is the application of Bayes' Theorem (Peebles 17-19). Classical statistics are concerned with the analysis of sampled data. The analysis of sampled data permits the researcher to make inferences concerning total populations, with the exclusion of any personal judgment or opinions. To the contrary, Bayesian statistics purposefully incorporates informed judgments into the analyses of data. Informed judgments are based on sound experience. While such judgments may be termed intuition, they are not irrational. This research examines the role of the inclusion of informed judgments based on experience into quantitative decisionmaking. This approach to quantitative analysis is called Bayesian statistics or Bayesian analysis (Zellner 5).