National Repository of Grey Literature 7 records found  Search took 0.00 seconds. 
Geometric algebras and neural networks
Zapletal, Jakub ; Procházková, Jana (referee) ; Vašík, Petr (advisor)
This thesis deals with the use of geometric algebras in the field of neural networks. First, Conformal Geometric Algebra (CGA) and Geometric Algebra for Conics (GAC) and their Python implementations are introduced. The functioning of neural networks is then described, including an explanatory example. Finally, both topics are connected by using the appropriate library in the Python language, and the possibilities of geometric algebras for different models of neural networks are demonstrated on several examples.
Gini coefficient maximization in binary logistic regression
Říha, Samuel ; Hanzák, Tomáš (advisor) ; Hlávka, Zdeněk (referee)
This Bachelor thesis describes a binary logistic regression model. By means of the term loss function a parameter estimation for the model is derived. A "rich" set of "proper" loss functions - beta family of Fisher-consistent loss functions - is defined. In the second part of the thesis, four basic goodness-of-fit criteria - Gini coefficient, C-statistics, Kolmogorov-Smirnov statistics and coefficient of determination R2 are defined. Further on, a possibility of parameter estimation by maximizing the Gini coefficient is analysed. Several algorithms are designed for this purpose. They are compared with so far existing methods in one simulated data set and three real ones. 1
Image segmentation of unbalanced data using artificial intelligence
Polách, Michal ; Rajnoha, Martin (referee) ; Kolařík, Martin (advisor)
This thesis focuses on problematics of segmentation of unbalanced datasets by the useof artificial inteligence. Numerous existing methods for dealing with unbalanced datasetsare examined, and some of them are then applied to real problem that consist of seg-mentation of dataset with class ratio of more than 6000:1.
Two-stage backtesting of Value-at-Risk models
Matyáš, Jan ; Seidler, Jakub (advisor) ; Brechler, Josef (referee)
Bachelor Thesis Two-stage backtesting of Value-at-Risk models Jan Matyáš Abstract This paper deals with a comparative evaluation of various Value-at-Risk models in terms of their prediction accuracy. We use two-stage backtesting procedure to find the most robust methodology in several aspects. Backtesting framework comprises of testing properties of independence, unconditional coverage, and conditional coverage and successive stage, that uses loss function allowing us to compare the two selected models from the previous part. Four European indices are taken to represent both well developed countries (DAX, ATX) and developing countries (PX, WIG). Models are examined over the period from January 1997 to February 2014. The best performing model in our selection appears to be the historical method with a 99% confidence interval. The use of stable distribution or lower confidence interval do not produce satisfactory results. Powered by TCPDF (www.tcpdf.org)
Economic Rationale for Damage Functions Entering the Social Cost of Carbon
Hochmann, Lukáš ; Havránek, Tomáš (advisor) ; Rečka, Lukáš (referee)
Climate change studies repeatedly report the present value damage from global warming in the realms of trillion USD. To adopt an efficient climate policy, precise estimates of the costs and damages are essential. This thesis aims to review the most influential social cost of carbon models and to propose for the first time a best practice approach to constructing the damage function. Based on the reliability of the key estimates, two alternative approaches are proposed. The first consists of deriving a highly universal damage function and conse- quent calibration by multiple point estimates. The latter is based on damage disaggregation to different sectors and subsequent single-point calibration of each contribution separately. Both approaches address the current challenges for the damage function - a flexible functional form and treatment of intangible damages. JEL Classification D62, D90, Q51, Q54 Keywords Social cost of carbon, SCC, damage function 1
Gini coefficient maximization in binary logistic regression
Říha, Samuel ; Hanzák, Tomáš (advisor) ; Hlávka, Zdeněk (referee)
This Bachelor thesis describes a binary logistic regression model. By means of the term loss function a parameter estimation for the model is derived. A "rich" set of "proper" loss functions - beta family of Fisher-consistent loss functions - is defined. In the second part of the thesis, four basic goodness-of-fit criteria - Gini coefficient, C-statistics, Kolmogorov-Smirnov statistics and coefficient of determination R2 are defined. Further on, a possibility of parameter estimation by maximizing the Gini coefficient is analysed. Several algorithms are designed for this purpose. They are compared with so far existing methods in one simulated data set and three real ones. 1
Inflation Targeting in the Czech Republic
Klukavý, Petr ; Koderová, Jitka (advisor) ; Langer, Miroslav (referee)
This diploma thesis is focused on the description of Inflation Targeting regime in the Czech Republic. The paper is divided into three parts. The first part deals with inflation and its targeting and with the economical circumstances that led to the launch of this monetary policy regime in the Czech Republic. The next part concerns the central bank reaction function, transmission channels, evaluation of the inflation target sets and the description of prognostic models that the central bank uses for forecasting. Then main stress is laid on the new structural dynamic model "g3". The last part describes my own inflation prognosis, which is based on the time series analysis.