Bayesian network

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Bayesian network

A form of artificial intelligence—named for Bayes’ theorem—which calculates probability based on a group of related or influential signs. Once a Bayesian network AI is taught the symptoms and probable indicators of a particular disease, it can assess the probability of that disease based on the frequency or number of signs in a patient.
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According to the results of the calculations, the variable describing the economy which in the period 2000-2013 was the most frequently reported in Bayes networks describing the relationship of the size of general government sector was the parameter that is called gross domestic product in current prices per inhabitant (GDP per inhabitant).
Subsequet positions were taken by the following ratios: FDI--foreign direct investment (million USD)--39 occurrences in networks, retail sales--dynamic index of turnover (total 2010 = 100)--37 occurrences, growth rates of GDP (percentage change)--36 occurrences, as well as inward FDI flows (million USD)--32 occurrences in Bayes networks and potential output of total economy (million euro)--30 occurrences.
In the case of the remaining ratios describing an economy and its relation with the sizes of the general government sector, the frequency of their occurrence in Bayes networks did not exceed 4 cases.
First of all, the research provided a creation of a ranking of the maximum number of occurrences for variables that describe an economy in Bayes networks (describing a relation of an economy to the size of the general government sector) in the examined period 2000-2013 (see Table 5).
Because of that, it was possible to find the answer to a question about the number of relations with parameters describing an economy that were correlated with 15 examined variables describing size of the general government sector in Bayes networks.
In this section we propose to examine decision trees, influence diagrams, and Bayes networks in turn and compare each with Wigmore diagrams.
57) Bayes networks enable one to process information and to move from a description of the problem predata to a description of the problem postdata.
Despite this, the standard exposition of both Bayes networks and decision trees assumes a defined problem and a predetermined set of influences.
The most obvious difference between Wigmore Charts and Bayes networks is that no mechanism for assessing probabilities exists in Wigmore Charts.
In fact, the similarities in concept between Bayes networks and Wigmore's "attempt at a working method" identify Wigmore as one of the unrecognized forefathers of modern decision analysis methods.
In drawing a Bayes network, in contrast, these generalizations tend to disappear into the conditional probability tables for the nodes.
We are excited to see this technology, which is based on our long-time experience in bayes networks and statistical models, being successfully applied in the biomedical field and opening new approaches for the better understanding of complex diseases," says Prof.