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Bayesian Network Modeling in Discovering Risk Factors of Dental Caries in Three-Year-Old Children

100%
Łaguna W., Bagińska J., Oniśko A.
Progress in Health Sciences
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2019
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vol. 1
118-125
EN
Purpose - The aim of this study was to use probabilistic graphical models to determine dental caries risk factors in three-year-old children. The analysis was conducted on the basis of the questionnaire data and resulted in building probabilistic graphical models to investigate dependencies among the features gathered in the surveys on dental caries. Materials and Methods - The data available in this analysis came from dental examinations conducted in children and from a questionnaire survey of their parents or guardians. The data represented 255 children aged between 36 and 48 months. Self-administered questionnaires contained 34 questions of socioeconomic and medical nature such as nutritional habits, wealth, or the level of education. The data included also the results of oral examination by a dentist. We applied the Bayesian network modeling to construct a model by learning it from the collected data. The process of Bayesian network model building was assisted by a dental expert. Results - The model allows to identify probabilistic relationships among the variables and to indicate the most significant risk factors of dental caries in three-year-old children. The Bayesian network model analysis illustrates that cleaning teeth and falling asleep with a bottle are the most significant risk factors of dental caries development in three-year-old children, whereas socioeconomic factors have no significant impact on the condition of teeth. Conclusions - Our analysis results suggest that dietary and oral hygiene habits have the most significant impact on the occurrence of dental caries in three-year-olds.
2
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Wykorzystanie sieci bayesowskich do prognozowania bankructwa firm

100%
Gąska D.
Śląski Przegląd Statystyczny
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2016
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issue 14 (20)
131-144
EN
The aim of the paper is to compare accuracy of some bankruptcy prediction models based on Bayesian networks. Some network structure learning algorithms were analyzed as a tool for classifiers construction. Empirical analysis was applied to companies listed on Warsaw Stock Exchange. The paper gives short overview of theoretical background behind discussed issues and presents results of empirical analysis.
3
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CEO emotional bias and dividend policy: Bayesian network method

75%
Mohamed Ali A., Anis J.
Business and Economic Horizons
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2012
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vol. 7
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issue 1
1-18
EN
This paper assumes that managers, investors, or both behave irrationally. In addition, even though scholars have investigated behavioral irrationality from three angles, investor sentiment, investor biases and managerial biases, we focus on the relationship between one of the managerial biases, overconfidence and dividend policy. Previous research investigating the relationship between overconfidence and financial decisions has studied investment, financing decisions and firm values. However, there are only a few exceptions to examine how a managerial emotional bias (optimism, loss aversion and overconfidence) affects dividend policies. This stream of research contends whether to distribute dividends or not depends on how managers perceive of the company’s future. I will use Bayesian network method to examine this relation. Emotional bias has been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some100 Tunisian executives. Our results have revealed that leader affected by behavioral biases (optimism, loss aversion, and overconfidence) adjusts its dividend policy choices based on their ability to assess alternatives (optimism and overconfidence) and risk perception (loss aversion) to create of shareholder value and ensure its place at the head of the management team.
4
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Probabilistyczny model oceny nabywania kompetencji zawodowych

63%
Wójciki T.
Edukacja Ustawiczna Dorosłych
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2014
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issue 3(86)
104-112
EN
The article presents an original competencies assessment model based on probabilistic methods. The key problems related to the diversity of competencies and functioning classifications are showed. Principles for the assessment of competencies using classical methods used by many research centers, companies and institutions in order to assess employees are presented. Competencies assessment model based on Bayesian network, which is directed graph, where the nodes represent specific attributes, whereas the edges represent the relationships, and the probability assigned to them, reflecting the structure of cause and effect for selected areas of domain are described. The principles for deter-mining the probability of obtaining a positive assessment for selected competencies, and the rules for calculating the total probability distribution for the whole structure of the graph, which is the basis of the model are presented. The advantages and limitations of the proposed probabilistic method are described.
PL
Zaproponowany model oceny nabycia kompetencji zawodowych bazuje na zało-żeniu, że posiadanie określonej wiedzy lub umiejętności nazwanych grupą kompeten-cji podstawowych ma wpływ na inne umiejętności i wiedzę tworzące tzw. grupę kom-petencji zależnych, powiązaną z grupą podstawową, dla której prowadzone są procesy oceny metodami klasycznymi. Powiązania pomiędzy umiejętnościami, wiedzą oraz innymi atrybutami znajdującymi się w grupie podstawowej a atrybutami znajdującymi się w grupie kompetencji zależnych reprezentowane są w postaci acyklicznego grafu skierowanego, zbudowanego z węzłów oraz łączących je krawędzi. Węzły odwzoro-wują określone atrybuty (np. umiejętności, wiedzę teoretyczną), natomiast krawędzie to relacje zachodzące pomiędzy poszczególnymi atrybutami z przypisanymi do nich określonymi stopniami prawdopodobieństwa. Graf taki odwzorowuje strukturę kompetencji dla wybranego obszaru dziedzinowego i nazywany jest również siecią Bayesa.
5
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Sieć Bayesa jako narzędzie wspomagające zarządzanie ryzykiem operacyjnym w banku

63%
Gadowska-dos Santos D.
Problemy Zarządzania
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2017
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issue 1/2017 (66), t.2
125-144
PL
W niniejszym artykule autor próbuje wykazać, że w procesie zarządzania ryzykiem operacyjnym w banku szczególnie istotne jest przeprowadzenie analizy źródeł ryzyka wraz z rozpoznaniem zależności przyczynowo- skutkowych. Jedynie gruntowna wiedza o powodach i konsekwencjach materializacji ryzyka daje bowiem szansę skutecznego prognozowania efektów podejmowanych działań zarządczych, planowania interwencji i poprzez to kształtowania rzeczywistości zgodnie z oczekiwaniami. Artykuł koncentruje się na zaprezentowaniu narzędzia badania łańcuchów przyczynowych – sieci Bayesa, które mogą pomóc bankom lepiej zrozumieć naturę ryzyka operacyjnego, zmniejszyć jego skalę i w efekcie zwiększyć efektywność działania instytucji. Zaprezentowana zostanie definicja, zasady konstrukcji, sposoby wykorzystania tej metody do analizy zależności przyczynowo-skutkowych pomiędzy czynnikami ryzyka operacyjnego, a także zalety i wady tego podejścia.
EN
This paper shows that analysis of risk sources and identification of cause-effect relationships are crucial elements of the operational risk management process. Knowledge of the reasons and consequences of risk materialization is key for reliable forecasting of the effects of managerial actions and for planning interventions capable of shaping the reality according to expectations. The article concentrates on presenting one means of analyzing causal chains – Bayesian networks that can help banks understand the nature of operational risk, minimizing its scale, and, as a result, increasing the financial institutions’ efficiency. The definition, design rules, ways of using the method to analyze cause-effect relationships between operational risk factors, as well as advantages and drawbacks of the approach, are discussed.
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