On the effective implementation of the iterative proportional fitting procedure
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Locally Strong Coherence in Inference Processes
Annals of Mathematics and Artificial Intelligence
Updating beliefs with incomplete observations
Artificial Intelligence
The role of coherence for handling probabilistic evaluations and independence
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Statistical Matching: Theory and Practice (Wiley Series in Survey Methodology)
Statistical Matching: Theory and Practice (Wiley Series in Survey Methodology)
Computing lower and upper expectations under epistemic independence
International Journal of Approximate Reasoning
Probabilistic abduction without priors
International Journal of Approximate Reasoning
De Finetti's contribution to the theory of random functions
International Journal of Approximate Reasoning
Correction of incoherent conditional probability assessments
International Journal of Approximate Reasoning
Credit scoring analysis using a fuzzy probabilistic rough set model
Computational Statistics & Data Analysis
Towards integrative causal analysis of heterogeneous data sets and studies
The Journal of Machine Learning Research
Incoherence correction strategies in statistical matching
International Journal of Approximate Reasoning
On an implicit assessment of fuzzy volatility in the Black and Scholes environment
Fuzzy Sets and Systems
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In several applications there is the need to consider different data sources and to integrate information: a specific case is the so-called statistical matching, where data sources have just a set of common variables and inference is required on the other variables. The traditional way to cope with such situations is to combine the available data with assumptions strong enough to identify pointwise the joint probability. Such assumptions cannot always be justified and inference should take into account all the set of compatible probabilities. In this paper, we show how statistical matching problems can be managed by means of coherent conditional probability: coherence allows us to combine the knowledge coming from different multiple sources, included those given from field experts, without necessarily assuming further hypothesis. Moreover, inferences and decisions can be dealt with by taking in consideration also logical constraints among the variables, which arise naturally in the applications. An example showing advantages and drawbacks of the proposed method is given.