|
|
Potential calculation of mutual information from a time series
Hubr, Ivo ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
Mutual information is one of the factors used in traffic analysis and preparation phase space. Begin of this work deal with information theory, focusing on the calculation of mutual information. To calculate this parameter has been available for many algorithms which are analyzing in this final work. Two of the algorithms (Fraser-Swinney and calculation of mutual information using adaptive XY subdivision) are applied to the input data Rössler’ attractor, as shown in the output tables and graphs. The third consideration method is the computational Dinh-Tuan-Pham algorithm. The main goal of this work is a comparison of efficiency, speed and accuracy of the calculation of these algorithms.
|
|
|
Detection of Correlated Mutations
Ižák, Tomáš ; Bendl, Jaroslav (referee) ; Martínek, Tomáš (advisor)
Tato práce zkoumá existující možnosti a metody detekce korelovaných mutací v proteinech. Práce začíná teoretickým úvodem do zkoumané problematiky. Využití informací o korelovaných mutacích je především při predikci terciální struktury proteinu či hledání oblastí s významnou funkcí. Dále následuje přehled v současnosti používaných metod detekce a jejich výhody a nevýhody. V této práci jsou zkoumány zejména metody založené na statistice (například Pearsonově korelačním koeficientu nebo Pearsonově chi^2 testu), informační teorii (Mutual information - MI) a pravděpodobnosti (ELSC nebo Spidermonkey). Dále jsou popsány nejdůležitější nástroje s informací o tom, které metody používají a jakým způsobem. Také je diskutována možnost návrhu optimálního algoritmu. Jako optimální z hlediska úspěšnosti detekce je doporučeno využít více zmíněných metod. Také je doporučeno při detekci využít fyzikálně-chemických vlastností aminokyselin. V praktické části byla vyvinuta metoda využívající fyzikálně-chemických vlastností aminokyselin a fylogenetických stromů. Výsledky detekce byly porovnány s nástroji CAPS, CRASP a CMAT.
|
|
|
Potential calculation of reciprocal information from a time series
Hubr, Ivo ; Rajmic, Pavel (referee) ; Kacálek, Jan (advisor)
Mutual information is one of the factors used in traffic analysis and preparation phase space. At the beginning of this work I deal with information theory, focusing on the theoretical calculation of mutual information. To calculate this parameter has been available for many algorithms which I analyze in my final work. Two of the algorithms (Fraser-Swinney and calculation of mutual information using adaptive XY subdivision) are applied to the input data Rössler' attractor, as shown in the output tables and graphs. The third consideration method is the computational Dinh-Tuan-Pham algorithm. The main goal of my work is a comparison of efficiency, speed and accuracy of the calculation of these algorithms.
|
|
|
O odhadu vzájemné informace
Marek, Tomáš ; Tichavský, Petr
The mutual information is useful measure of a random vector component dependence. It is important in many technical applications. The estimation methods are often based on the well known relation between the mutual information and the appropriate entropies. In 1999 Darbellay and Vajda proposed a direct estimation methods. In this paper we compare some available estimation methods using different 2-D random distributions.
|
|
|
Chaotic time-series prediction
Dědič, Martin ; Tichý, Vladimír (advisor) ; Smrčka, Pavel (referee)
This thesis focuses on possibility of chaotic (specially economic) time-series prediction. Chaotic time-series are unpredictable in long-term due to their high sensitivity on initial conditions. Nevertheless, their behavior should be more or less predictable in short-term. Goal of this thesis is to show, how much and if any prediction, is possible by non-linear prediction method, and try to reveal or to reject presence of chaotic behavior in them. Work is split into three chapters. Chapter One briefly introduces chosen important concepts and methods from this area. In addition, to describe some prediction methods, there are outlined which indicators and methods are possible to use in order to find possibilities and boundaries of this prediction. Chapter Two is focused on modifications of FracLab software, which is used for create this prediction. Last chapter is experimental. Besides the description of examined time-series and methods, it includes discussion of results.
|
guest ::
