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An empirical comparison of popular algorithms for learning gene networks
Djordjilović, V. ; Chiogna, M. ; Vomlel, Jiří
In this work, we study the performance of different algorithms for learning gene networks from data. We consider representatives of different structure learning approaches, some of which perform unrestricted searches, such as the PC algorithm and the Gobnilp method and some of which introduce prior information on the structure, such as the K2 algorithm. Competing methods are evaluated both in terms of their predictive accuracy and their ability to reconstruct the true underlying network. A real data application based on an experiment performed by the University of Padova is also considered. We also discuss merits and disadvantages of categorizing gene expression measurements.
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Influence diagrams for speed profile optimization" computational issues
Vomlel, Jiří ; Kratochvíl, Václav
Influence diagrams were applied to diverse decision problems. However, the general theory is still not sufficiently developed if the variables are continuous or hybrid and the utility functions are nonlinear. In this paper, we study computational problems related to the application of influence diagrams to vehicle speed profile optimization and suggest an approximation of the nonlinear utility functions by piecewise linear functions.
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Proceedings of the 10th Workshop on Uncertainty Processing
Kratochvíl, Václav
WUPES 2015 is organized jointly by the Institute of Information Theory and Automation of the Czech Academy of Sciences and by the Faculty of Management, University of Economics, Prague. It is quite natural that such a meeting could not materialize if it were not for the hard work of many our colleagues and friends. This is why we want to express our gratitude to all the members of both the Programme and Organizing Committees. Last but not least, we also want to acknowledge the fact that this workshop is organized, due to the fact that the research of several members of the Organizing Committee is financially supported by grants GA CR no 15-00215S and 13-20012S.
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On Linear Probabilistic Opinion Pooling Based on Kullback-Leibler Divergence
Sečkárová, Vladimíra
In this contribution we focus on the finite collection of sources, providing their opinions about a hidden (stochastic) phenomenon, that is not directly observable. The assumption on obtaining opinions yields a decision making process commonly referred to as opinion pooling. Due to the complexity of the space of possible decisions we consider the probability distributions over this set rather than single values, exploited before, e.g., in [2]. The final decision (result of pooling) is then a combination of probability distributions provided by sources.
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Homomorphic Coordinates of Dempster’s Semigroup
Daniel, Milan
Coordinates of belief functions on two-element frame of discernment are defined using homomorphisms of Dempster’s semigroup (the algebra of belief functions with Dempster’s rule). Three systems of the coordinates (h-f, h-f0, and coordinates based on decomposition of belief functions) are analysed with a focus to their homomorphic properties. Further, ideas of generalisation of the investigated systems of coordinates to general finite frame of discernment are presented.
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Algorithms for single-fault troubleshooting with dependent actions
Lín, Václav
We study the problem of single-fault troubleshooting with dependent actions. We propose a binary integer programming formulation for the problem. This can be used to solve the problem directly or to compute lower bounds of optima using linear programming relaxation. We present an optimal dynamic programming algorithm, and three greedy algorithms for computing upper bounds of optima.
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