How to down-weight observations in robust regression: A metalearning study
Kalina, Jan; Pitra, Z.
2018 - English Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination. Keywords: metalearning; robust statistics; linear regression; outliers Fulltext is available at external website. How to down-weight observations in robust regression: A metalearning study

Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is ...

Kalina, Jan; Pitra, Z.
Ústav teorie informace a automatizace, 2018

About Two Consonant Conflicts of Belief Functions
Daniel, M.; Kratochvíl, Václav
2018 - English General belief functions usually bear some internal conflict which comes mainly from disjoint focal elements. Analogously, there is often some conflict between two (or more) belief functions. After the recent observation of hidden conflicts (seminar CJS’17 [17]), appearing at belief functions with disjoint focal elements, importance of interest in conflict of belief functions has increased. This theoretical contribution introduces a new approach to conflicts (of belief functions). Conflicts are considered independently of any combination rule and of any distance measure. Consonant conflicts are based on consonant approximations of belief functions in general; two special cases of the consonant approach based on consonant inverse pignistic and consonant inverse plausibility transforms are discussed. Basic properties of the newly defined conflicts are presented, analyzed and briefly compared with our original approaches to conflict (combinational conflict, plausibility conflict and comparative conflict), with the recent conflict based on non-conflicting parts, as well as with W. Liu’s degree of conflict. Keywords: belief function; conflict; consonant Fulltext is available at external website. About Two Consonant Conflicts of Belief Functions

General belief functions usually bear some internal conflict which comes mainly from disjoint focal elements. Analogously, there is often some conflict between two (or more) belief functions. After ...

Daniel, M.; Kratochvíl, Václav
Ústav teorie informace a automatizace, 2018

Simulace možného výskytu horkých míst při únicích radioaktivity při neobvyklých\npovětrnostních epizodách
Pecha, Petr; Tichý, Ondřej
2018 - Czech Analyzovány mimořádné úniky škodlivin při nízkých rychlostech větru až bezvětří (calmy) u lokalit vytypovanými v ČR jako potenciální hlubinná úložiště vyhořelého jaderného paliva. Kontinuální únik a současně i jeho časová dynamika jsou aproximovány sekvencí diskrétních 3-D Gaussovských obláčků. Scénář pokračuje po několika hodinách nástupem konvektivního proudění, kdy nehybná oblast s relativně vysokou kumulovanou radioaktivitou je rozfoukávána větrem. V simulačním běhu zavedeme spekulativní předpoklad, že ve 3. hodině navazující konvektivní fáze prší. Objeví výrazné horké místo deponovaného Cs137 na zemském povrchu až ve vzdálenosti desítky kilometrů od původního zdroje úniku. Simulation of accidental releases of radioactiity during calm meteorological situations in relations with planned nuclear spent fuel storages. Approximation of discrete Gaussian puffs is introduced. After some time the stationary calm field is assumed to be scattered by wind flow. In the 3dr hour of the wind-flow phase under rain commencement some surprised hot spots of CS137 on terrain have occurred in rather far distances from the source of discharges. Keywords: Nuclear; spent fuel; release; radiotoxicity Fulltext is available at external website. Simulace možného výskytu horkých míst při únicích radioaktivity při neobvyklých\npovětrnostních epizodách

Analyzovány mimořádné úniky škodlivin při nízkých rychlostech větru až bezvětří (calmy) u lokalit vytypovanými v ČR jako potenciální hlubinná úložiště vyhořelého jaderného paliva. Kontinuální únik a ...

Pecha, Petr; Tichý, Ondřej
Ústav teorie informace a automatizace, 2018

Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions
Plajner, Martin; Vomlel, Jiří
2018 - English Learning parameters of a probabilistic model is a necessary step in most machine learning modeling tasks. When the model is complex and data volume is small the learning process may fail to provide good results. In this paper we present a method to improve learning results for small data sets by using additional information about the modelled system. This additional information is represented by monotonicity conditions which are restrictions on parameters of the model. Monotonicity simplifies the learning process and also these conditions are often required by the user of the system to hold. \n\nIn this paper we present a generalization of the previously used algorithm for parameter learning of Bayesian Networks under monotonicity conditions. This generalization allows both parents and children in the network to have multiple states. The algorithm is described in detail as well as monotonicity conditions are.\n\nThe presented algorithm is tested on two different data sets. Models are trained on differently sized data subsamples with the proposed method and the general EM algorithm. Learned models are then compared by their ability to fit data. We present empirical results showing the benefit of monotonicity conditions. The difference is especially significant when working with small data samples. The proposed method outperforms the EM algorithm for small sets and provides comparable results for larger sets. Keywords: Bayesian networks; Learning model parameters; monotonicity condition Fulltext is available at external website. Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions

Learning parameters of a probabilistic model is a necessary step in most machine learning modeling tasks. When the model is complex and data volume is small the learning process may fail to provide ...

Plajner, Martin; Vomlel, Jiří
Ústav teorie informace a automatizace, 2018

Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
Kalina, Jan
2018 - English The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation coefficient based on the least weighted squares regression and the minimum weighted covariance determinant estimator, where the latter allows to estimate the mean and covariance matrix of multivariate data. New tools are proposed allowing to test hypotheses about these robust estimators or to estimate their variance. The techniques considered in the paper include resampling approaches with or without replacement, i.e. permutation tests, bootstrap variance estimation, and bootstrap confidence intervals. The performance of the newly described tools is illustrated on numerical examples. They reveal the suitability of the robust procedures also for non-contaminated data, as their confidence intervals are not much wider compared to those for standard maximum likelihood estimators. While resampling without replacement turns out to be more suitable for hypothesis testing, bootstrapping with replacement yields reliable confidence intervals but not corresponding hypothesis tests. Keywords: robust statistics; multivariate data; correlation coefficient; econometrics Fulltext is available at external website. Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators

The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation ...

Kalina, Jan
Ústav teorie informace a automatizace, 2018

Vyhodnocování grantové soutěže pomocí otevřené expertní databáze
Grim, Jiří
2018 - Czech Keywords: open expert database; research funding; grants Fulltext is available at external website. Vyhodnocování grantové soutěže pomocí otevřené expertní databáze

Grim, Jiří
Ústav teorie informace a automatizace, 2018

SYNTHESIZED ENRICHMENT FUNCTIONS FOR EXTENDED FINITE ELEMENT ANALYSES WITH FULLY RESOLVED MICROSTRUCTURE
Doskar, M.; Novák, J.; Zeman, Jan
2017 - English Inspired by the first order numerical homogenization, we present a method for extracting continuous fluctuation fields from the Wang tile based compression of a material microstructure. The fluctuation fields are then used as enrichment basis in Extended Finite Element Method (XFEM) to reduce number of unknowns in problems with fully resolved microstructural geometry synthesized by means of the tiling concept. In addition, the XFEM basis functions are taken as reduced modes of a detailed discretization in order to circumvent the need for non-standard numerical quadratures. The methodology is illustrated with a scalar steady-state problem. Keywords: Wang tiling; microstructure synthesis; microstructure-informed enrichment functions; extended finite element method Fulltext is available at external website. SYNTHESIZED ENRICHMENT FUNCTIONS FOR EXTENDED FINITE ELEMENT ANALYSES WITH FULLY RESOLVED MICROSTRUCTURE

Inspired by the first order numerical homogenization, we present a method for extracting continuous fluctuation fields from the Wang tile based compression of a material microstructure. The ...

Doskar, M.; Novák, J.; Zeman, Jan
Ústav teorie informace a automatizace, 2017

Question Selection Methods for Adaptive Testing with Bayesian Networks
Plajner, Martin; Magauina, A.; Vomlel, Jiří
2017 - English The performance of Computerized Adaptive Testing systems, which are used for testing of human knowledge, relies heavily on methods selecting correct questions for tested students. In this article we propose three different methods selecting questions with Bayesian networks as students’ models. We present the motivation to use these methods and their mathematical description. Two empirical datasets, paper tests of specific topics in mathematics and Czech language for foreigners, were collected for the purpose of methods’ testing. All three methods were tested using simulated testing procedure and results are compared for individual methods. The comparison is done also with the sequential selection of questions to provide a relation to the classical way of testing. The proposed methods are behaving much better than the sequential selection which verifies the need to use a better selection method. Individually, our methods behave differently, i.e., select different questions but the success rate of model’s predictions is very similar for all of them. This motivates further research in this topic to find an ordering between methods and to find the best method which would provide the best possible selections in computerized adaptive tests. Keywords: Computerized Adaptive Testing; Question Selection Methods; Bayesian Networks Fulltext is available at external website. Question Selection Methods for Adaptive Testing with Bayesian Networks

The performance of Computerized Adaptive Testing systems, which are used for testing of human knowledge, relies heavily on methods selecting correct questions for tested students. In this article we ...

Plajner, Martin; Magauina, A.; Vomlel, Jiří
Ústav teorie informace a automatizace, 2017

Analysis of truncated data with application to the operational risk estimation
Volf, Petr
2017 - English Analysis of operational risk often faces problems arising from the structure of available data, namely of left truncation and occurrence of heavy-tailed loss values. We deal with model given by lognormal dostribution contaminated by the Pareto one and to use of the Cramér-von Mises, Anderson-Darling, and Kolmogorov-Smirnov minimum distance estimators. Analysis is based on MC studies. The main objective is to propose a method of statistical analysis and modeling for the distribution of sum of\nlosses over a given period, particularly of its right quantiles. Keywords: operational risk; statistical analysis; truncated data Fulltext is available at external website. Analysis of truncated data with application to the operational risk estimation

Analysis of operational risk often faces problems arising from the structure of available data, namely of left truncation and occurrence of heavy-tailed loss values. We deal with model given by ...

Volf, Petr
Ústav teorie informace a automatizace, 2017

Appearance Acquisition and Analysis of Effect Coatings
Filip, Jiří; Maile, F. J.
2017 - English Keywords: effect coatings; appearance capturing; polychromatic; particle orientation Fulltext is available at external website. Appearance Acquisition and Analysis of Effect Coatings

Filip, Jiří; Maile, F. J.
Ústav teorie informace a automatizace, 2017