Bayes estimates of muIti‐criteria decision alternatives using Monte Carlo integration

C. G. E. Boender and Herman van Dijk

Statistica Neerlandica, 1993, vol. 47, issue 2, 127-151

Abstract: A Bayesian procedure is proposed for the estimation of the weights of the alternatives in a multi‐criteria decision model with data that stem from pair‐wise comparison of alternatives. The prior information restricts the weights to the unit simplex. The posterior results are computed by Monte Carlo integration procedures based on importance sampling. The Bayesian procedure is applied to a case study concerning the choice of a professor of Operations Research (OR). Results are: (1) according to the Bayesian procedure a different candidate would be chosen as professor of OR than according to the maximum likelihood procedure; (2) given the prior and data information, there exists a substantial probability of taking the wrong decision; (3) there exists a ranking of the candidates with a posterior probability greater than one half.

Date: 1993
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