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Integrating inconsistent data in a probabilistic model. (English) Zbl 1185.68699
Summary: In this paper we discuss the process of building a joint probability distribution from an input set of low-dimensional probability distributions. Since the solution of the problem for a consistent input set of probability distributions is known we concentrate on a setup where the input probability distributions are inconsistent. In this case the iterative proportional fitting procedure, which converges in the consistent case, tends to come to cycles. We propose a new algorithm that converges even in inconsistent case. The important property of the algorithm is that it can be efficiently implemented exploiting decomposability of considered distributions.

68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence
68Q87 Probability in computer science (algorithm analysis, random structures, phase transitions, etc.)
Full Text: DOI
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