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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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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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Real-time diagnostics for ROS running systems based on probabilistic patterns identification
Věchet, Stanislav ; Krejsa, Jiří
Autonomous mobile robots consists of various software modules to achieve given goal, including solving complex navigation tasks as localization, mapping or path planning. These tasks are highly dependent on the quality of data measured and gathered from hardware subsystems. Using Robot Operating System (ROS) as integration basis reduces the development effort and time to market. While ROS framework itself is considered as reliable and stable to run even soft real-time tasks, in case of any internal failures on data misreadings can be problematic to debug or even identify the problem for common user. Due to this unpleasant situations we develop a virtual assistant, internally represented as diagnostic expert system, to help users to identify and possibly fix the problem.
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Návrh řešení studeného startu doporučovacího systému
MAŠTALÍŘ, Jakub
The main topic of the bacholor´s thesis is design and implementation of the recommender system´s module in the field of tourism and travelling. In the theoretical part the goal is mapping out an environment of the recommender system, their types and approaches. Furthermore there is described the problem of cold start and the theory of Bayes networks on the basis of which the data for the recommendation will be processed and presented. In the practical part we are programming and testing the recommender module. There is a description of the used technologies and individual functionalities of the recommender system aimed at the acommodation in České Budějovice are dismembered.
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Use of Bayesian Networks in Managerial Practice
Rod, Martin ; Váchová, Lucie (advisor) ; Bína, Vladislav (referee)
The main goal of our bachelor thesis is to create a quantitave model which describes real biogas plant (property of company VOD Kadov) and its troubleshooting. Our created model has broad applications. It can be used as a support tool for managerial decision making either for biogas plant's malfunction or for its improvement. Secondly it plays a major role in quantitative clarification of inner links and processes which takes place inside the biogas plant. This quantification is done by Bayesian statistic approach via Bayesian network and its methods. For modeling purposes we exploit (leak) noisy OR-gate model and various methods for variable discretization. We heavily use company's data for model creation. With our model we simulated the run of biogas plant and its possible malfunctions. We also provided an easy step by step guide how to troubleshoot these malfunctions.
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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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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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Aplikace bayesovských sítích ve hře Minesweepe
Vomlelová, M. ; Vomlel, Jiří
We use the computer game of Minesweeper to illustrate few modeling tricks utilized when applying Bayesian network (BN) models in real applications. Among others, we apply rank-one decomposition (ROD) toconditional probability tables (CPTs) representing addition. Typically, this transformation helps to reduce the computational complexity of probabilistic inference with the BN model. However, in this paper we will see that (except for the total sum node) when ROD is applied to the whole CPT it does not bring any savings for the BN model of Minesweeper. Actually, in order to gain from ROD we need minimal rank-one decompositions of CPTs when the state of the dependent variable is observed. But this is not known and it is a topic for our future research.
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Characterization of inclusion neighbourhood in terms of the essential graph: Lower neighbours
Studený, Milan
The topic of the paper is to characterize inclusion neighbourhood of a given equivalence class of Bayesian networks in terms of the respective essential graph. It is shown that every inclusion neighbour is uniquely described by a pair ([a,b],C) where [a,b] is a pair of distict nodes which is not an edge and C is a disjoint set of nodes. Given such [a,b] the collection of respective sets C is the union of two tufts. The least and maximal sets of these tufts can be read from the essential graph.
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