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A Tool for Administration of the Company Product Portfolio
Koreň, Miroslav ; Rychlý, Marek (referee) ; Květoňová, Šárka (advisor)
This paper concerns about key business process in the production companies, namely, the new product development. The object of this thesis has been to create a tool to estimate the risk of the new product development. To reach this goal, current tools used to deciding the risk must have been explored. As the best tool, appropriate for assessing the risk of new product development has proved the Bayesian Network. This paper explains the construction of the Bayesian network and shows the way how to generate the probabilities in the network to be accurate for the risk estimation. Based on this theoretical knowledge has been built an information system, which estimates the risk of the new products and administer the risks.
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Inference in Bayesian Networks
Šimeček, Josef ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This master's thesis deals with demonstration of various approaches to probabilistic inference in Bayesian networks. Basics of probability theory, introduction to Bayesian networks, methods for Bayesian inference and applications of Bayesian networks are described in theoretical part. Inference techniques are explained and complemented by their algorithm. Techniques are also illustrated on example. Practical part contains implementation description, experiments with demonstration applications and conclusion of the results.
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Jak čelit nejistotě při rozhodování o odstranění fosforu ve vodohospodářství
Brabec, Jan ; Vojáček, Ondřej (advisor) ; Zajíček, Miroslav (referee)
Implementation of EU Water Framework Directive has led to an increased demand for cost-benefit analysis in water management. The directive introduces a good status, which is required on all water bodies by 2027. Excessive phosphorus inflows are one of the main reasons for not meeting the criteria in the Czech Republic. If achieving of the good status is not cost-proportionate, exemption can be applied. Many different methodologies were created across different states, including Czech official methodology by Slavíková et al. (2015). However, this methodology does not deal with uncertainty of measures effectiveness. This thesis describes how to implement the uncertainty into calculations using Bayesian networks. A case study of Stanovice water reservoir demonstrates the approach practically. Results of the Bayesian network show, that selected measures with available data eliminate desired amount of phosphorus in 70% of all cases. This reduction is most likely sufficient, because it holds for the upper estimate of required abatement (60 to 200 kg). Based on comparison of benefits and costs, it seems net benefits are generated by implementing suggested measures. Therefore, policy recommendation is to implement the selected measures.
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On possible approaches to detecting robotic activity of botnets
Prajer, Richard ; Palovský, Radomír (advisor) ; Pavlíček, Luboš (referee)
This thesis explores possible approaches to detecting robotic activity of botnets on network. Initially, the detection based on full packet analysis in consideration of DNS, HTTP and IRC communication, is described. However, this detection is found inapplicable for technical and ethical reasons. Then it focuses on the analysis based on network flow metadata, compiling them to be processable in machine learning. It creates detection models using different machine learning methods, to compare them with each other. Bayes net method is found to be acceptable for detecting robotic activity of botnets. The Bayesian model is only able to identify the botnet that already executes the commands sent by its C&C server. "Sleeping" botnets are not reliably detectable by this model.
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A Tool for Administration of the Company Product Portfolio
Koreň, Miroslav ; Rychlý, Marek (referee) ; Květoňová, Šárka (advisor)
This paper concerns about key business process in the production companies, namely, the new product development. The object of this thesis has been to create a tool to estimate the risk of the new product development. To reach this goal, current tools used to deciding the risk must have been explored. As the best tool, appropriate for assessing the risk of new product development has proved the Bayesian Network. This paper explains the construction of the Bayesian network and shows the way how to generate the probabilities in the network to be accurate for the risk estimation. Based on this theoretical knowledge has been built an information system, which estimates the risk of the new products and administer the risks.
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Inference in Bayesian Networks
Šimeček, Josef ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This master's thesis deals with demonstration of various approaches to probabilistic inference in Bayesian networks. Basics of probability theory, introduction to Bayesian networks, methods for Bayesian inference and applications of Bayesian networks are described in theoretical part. Inference techniques are explained and complemented by their algorithm. Techniques are also illustrated on example. Practical part contains implementation description, experiments with demonstration applications and conclusion of the results.
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Stochastické metody v řízení projektů
Zemenová, Hana ; Fiala, Petr (advisor) ; Fábry, Jan (referee)
Každý projekt je ze své povahy spojen s jistou dávkou rizika a nejistoty, kterou je nutné zohlednit při volbě adekvátních metod pro jeho řízení. Cílem práce je tyto metody klasifikovat, porovnat a aplikovat na případové studii z podnikové praxe. Podrobněji jsou přitom rozebrány právě ty metody, které byly vhodné pro zkoumaný projekt z případové studie: jedná se o metodu CPM/PERT, simulaci Monte Carlo a analýzu projektu prostřednictvím bayesovských sítí.
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