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Foundations of compositional models: inference
International Journal of General Systems ( IF 2 ) Pub Date : 2021-03-25 , DOI: 10.1080/03081079.2021.1895142
Vl. Bína 1 , R. Jiroušek 1, 2 , V. Kratochvíl 1, 2
Affiliation  

Compositional models, as an alternative to Bayesian networks, are assembled from a system of low-dimensional distributions. Thus the respective apparatus falls fully into probability theory. The present paper surveys the results supporting the design of computational procedures, without which the application of these models to practical problems would be impossible.

The methods of inference cannot do without a possibility to focus on a part of the considered multidimensional model and to incorporate data describing the actual situation. Thus the paper shows how to compute marginals and conditionals of multidimensional models. Also, the paper briefly solves the problem of computing the effect of an intervention, in case the model is interpreted as a causal model.



中文翻译:

成分模型的基础:推论

组成模型是贝叶斯网络的替代模型,是从低维分布系统中组装而成的。因此,各个装置完全属于概率论。本文调查了支持计算程序设计的结果,没有这些结果,就不可能将这些模型应用于实际问题。

推论方法不可能没有将精力集中在所考虑的多维模型的一部分上,并且无法结合描述实际情况的数据。因此,本文展示了如何计算多维模型的边际和条件。此外,在模型被解释为因果模型的情况下,本文还简要解决了计算干预效果的问题。

更新日期:2021-05-18
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