Abstract
Any field of social sciences is based on uncertain knowledge, uncertain information and uncertain data. The economics is not an exception. This is why probability theory and probabilistic modeling play an important role in econometrics. In practical applications one has to cope with the fact that even relatively small models have to take into account rather hundreds than tens of factors. This is why the methods for multidimensional probability distribution representation, like Bayesian networks, have become so popular in this field. The goal of this paper is to promote an alternative approach, so called compositional models.
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Jiroušek, R. (2013). Brief Introduction to Probabilistic Compositional Models. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S., Suriya, K. (eds) Uncertainty Analysis in Econometrics with Applications. Advances in Intelligent Systems and Computing, vol 200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35443-4_4
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DOI: https://doi.org/10.1007/978-3-642-35443-4_4
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