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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.

MSC:
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.)
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