Modeling Expert Opinion Arising as a Partial Probabilistic Specification Academic Article uri icon

abstract

  • Expert opinion is often sought with regard to unknowns in a decision-making setting. For a univariate unknown, , our presumption is that such opinion is elicited as a partial probabilistic specification in the form of either probability assignments regarding the chance of falling in a fixed set of disjoint exhaustive intervals or selected quantiles for . Treating such specification as data, our focus is on the development of suitable probability densities for these data given the true . In particular, we advocate a rich class of densities created by transformation of random mixtures of beta distributions. These densities become likelihoods when viewed as a function of given the data. We presume that a decision-maker (here a so-called supra Bayesian) presides over the opinion collection, offering his or her assessment as well. All of this opinion is synthesized using Bayess theorem, resulting in the posterior distribution as the pooling mechanism. The models are applied to opinion collected regarding points per game for participants in the 1991 National Basketball Association championship basketball series. 1995 Taylor & Francis Group, LLC.

published proceedings

  • Journal of the American Statistical Association

author list (cited authors)

  • Gelfand, A. E., Mallick, B. K., & Dey, D. K.

citation count

  • 36

complete list of authors

  • Gelfand, Alan E||Mallick, Bani K||Dey, Dipak K

publication date

  • January 1995