A maximum entropy estimate of a wine rating’s distribution: results of tests using large samples of blind triplicates

Abstract

An acute difficulty in wine-ratings-related research, and in calculating consensus among judges, is that each rating is one observation drawn from a latent probability distribution that is wine- and judge-specific. One observation is almost no data at all. Minimizing a weighted sum of cross entropies has been proposed as a method of estimating the shape of the latent distribution using one observation. That method was tested on 30 blind triplicate wine ratings. This article replicates that test, presents results of tests at hypothetical boundary conditions, and then presents test results for a set of 30 blind triplicate ratings published by Cicchetti [(2014, August). Blind tasting of South African wines: A tale of two methodologies (AAWE Working Paper No. 164, 15 pages)] and a set of 1599 blind triplicate ratings from the California State Fair. The test results show that the minimum cross entropy solution is substantially more accurate than the analysis of a single rating.

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