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Bayesian Networks Applications
Chaloupka, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This master's thesis deals with possible applications of Bayesian networks. The theoretical part is mainly of mathematical nature. At first, we focus on general probability theory and later we move on to the theory of Bayesian networks and discuss approaches to inference and to model learning while providing explanations of pros and cons of these techniques. The practical part focuses on applications that demand learning a Bayesian network, both in terms of network parameters as well as structure. These applications include general benchmarks, usage of Bayesian networks for knowledge discovery regarding the causes of criminality and exploration of the possibility of using a Bayesian network as a spam filter.
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Neural Networks and Their Applications
Chaloupka, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
The aim of this thesis is to present a consistent insight into the most frequently used types of artificial neural networks and their applications. It depicts feedforward neural networks with backpropagation training algorithm, Hopfield networks and self-organizing maps (Kohonen maps). Second part of this thesis demonstrates typical applications of described networks and discusses various factors, which influence performance of these networks on chosen tasks.
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Nowcasting as the new potential predicting method for the policy-makers
Chaloupka, David ; Kofroň, Jan (advisor) ; Parízek, Michal (referee)
This bachelor's thesis examines the role of nowcasting as a real-time data pre- diction approach in policy-making. The study compares the informative value of macroeconomic forecasting during crises and the potential of nowcasting as an alternative. By analyzing selected forecasting and nowcasting indexes from 2019 to 2022, the research fnds that nowcasting performs well during during 2020, which was afected by COVID-19 pandemic. However, it faces limitations in terms of prediction horizon and data requirements. Although forecasting may lose its informative value during crises, nowcasting cannot entirely replace it. Instead, both approaches can complement each other, enhancing policy decisions. The thesis also highlights nowcasting's potential for policy analysis and also its use in science.
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In Collaboration with Despot: Analysing the Impact of Sanctions on Russia and the Eurasian Economic Union
Chaloupka, David ; Semerák, Vilém (advisor) ; Teichman, Jiří (referee)
This bachelor's thesis is focused on investigating the Eurasian Economic Union, which was formed in 2015 to deepen economic integration in the post-Soviet region. The research aims to analyse the impact of sanctions on Russia and its partners within the Eurasian Economic Union to identify if Russia is circum- venting sanctions through these states before the war in Ukraine in 2022. For the analysis, I use gravity equations and trade fow data aggregated on product level. Based on the fndings, I reveal that the sanctions imposed on Russia have negatively impacted trade with partner states, with average reductions of over 27%. Additionally, on one hand, the Eurasian Economic Union experienced a trade creation and trade diversion efect, but on the other hand, the imposition of sanctions after 2014 resulted in negative impacts on trade between member states and with third states.
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Some aggressive rock environment in the area of Prague, the groundwater and the associated protection of building structures
Štěpán, Jiří ; Král, Jan (advisor) ; Chaloupka, David (referee)
This master thesis deals with groundwater aggressiveness on one sheet of detailed engineering geology map of Prague 1:5000, sheet Praha 2-3. The aim of this thesis si an effort to verify how much geological environment participates in chemical composition of groundwater. The chemical analyses were evaluated using limit values stated in valid norm ČSN EN 206 Concrete - Specification, performance, production and conformity. The chemical analyses that analysed aggressive components (SO4 2- , CO2, pH, NH4 + , Mg2+ ) and total dissolved solids discovered that the geological environment affects the chemical composition of groundwater very strongly. The chemical composition of groundwater is influenced by groundwater velocity and therefore by the time which the water stays in contact with the geological environment, by local composition of the rock and its accessories especially pyrite. Groundwater in Ordovician formations in the area of interest is usually contains high amount of dissolved solids and it is calcium-sulphate. Groundwater of Dobrotiv and Libeň formations are significantly less aggressive than groundwater of Letná, Vinice and Zahořany formation. The chemical composition of groundwater in Peruc-Korycany formation corresponds with Letná formation on which it lies.
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Neural Networks and Their Applications
Chaloupka, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
The aim of this thesis is to present a consistent insight into the most frequently used types of artificial neural networks and their applications. It depicts feedforward neural networks with backpropagation training algorithm, Hopfield networks and self-organizing maps (Kohonen maps). Second part of this thesis demonstrates typical applications of described networks and discusses various factors, which influence performance of these networks on chosen tasks.
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Bayesian Networks Applications
Chaloupka, David ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This master's thesis deals with possible applications of Bayesian networks. The theoretical part is mainly of mathematical nature. At first, we focus on general probability theory and later we move on to the theory of Bayesian networks and discuss approaches to inference and to model learning while providing explanations of pros and cons of these techniques. The practical part focuses on applications that demand learning a Bayesian network, both in terms of network parameters as well as structure. These applications include general benchmarks, usage of Bayesian networks for knowledge discovery regarding the causes of criminality and exploration of the possibility of using a Bayesian network as a spam filter.
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