An essential component in Machine Learning processes is to estimate any unc
ertainty measure reflecting the strength of the relationships between varia
bles in a dataset. In this paper we focus on those particular situations wh
ere the dataset has incomplete entries, as most real-life datasets have. We
present a new approach to tackle this problem. The basic idea is to initia
lly estimate a set of probability intervals that will be used to complete t
he missing values. Then, these values are used to obtain new bounds of the
expected number of entries in the dataset, The probability intervals are na
rrowed iteratively until convergence, We have shown that the same processes
can be used to estimate both, probability intervals and probability distri
butions, and give conditions that guarantee that the estimator is the corre
ct one. (C) 2001 Elsevier Science Inc. All rights reserved.