  In this paper we address the problem of using one probability space for
estimating parameters and predicting future data when the observed data come
from multiple contexts and thus from distinct spaces. We explain that a
set-based probabilistic space might be suboptimal in the case of multiple
contexts. To overcome suboptimality and reconcile multiple contexts in one
space, the paper introduces the Quantum Probability Space (QPS). We also
present an algorithm to calculate the QPS for data observed from multiple
contexts and provide a web application that implements the algorithm. The QPS
has application in Information Retrieval (IR), Machine Learning (ML) and in any
domain where items should optimally be ranked and classified by some properties
but under conditions of uncertainty due to context.
