Multidimensional probabilistic models of behavior following similarity and choice judgments have proven to be useful in representing multidimensional percepts in Euclidean and non-Euclidean spaces. With few exceptions, these models are generally computationally intense because they often require numerical work with multiple integrals. This paper focuses attention on a particularly general triad and preferential choice model previously requiring the numerical evaluation of a 2n-fold integral, where n is the number of elements in the vectors representing the psychological magnitudes. Transforming this model to an indefinite quadratic form leads to a single integral. The significance of this form to multidimensional scaling and computational efficiency is discussed.
This article appears as:
Mullen, K. and Ennis, D. M.(1991). A simple multivariate probabilistic model for preferential and triadic choices. Psychometrika, 56(1), 69-75.
Colleagues can request this journal article here:
A simple multivariate probabilistic model for preferential and triadic choices
Not a Colleague? Click here to join for free!