Number of found documents: 667
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Selective Attention in Exchange Rate Forecasting
Kapounek, S.; Kučerová, Z.; Kočenda, Evžen
2020 - English
We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016. We employ a dynamic model averaging approach to reduce model selection uncertainty and to identify time-varying probability to include regressors in our models. Our results show that smaller sizes models accounting for the presence of selective attention offer improved fitting and forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the euro/dollar exchange rate following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs. Keywords: exchange rate; selective attention; news; forecasting; dynamic model averaging Fulltext is available at external website.
Selective Attention in Exchange Rate Forecasting

We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market ...

Kapounek, S.; Kučerová, Z.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach
Švorc, Jan; Vomlel, Jiří
2020 - English
The objective of this paper is to explore the associations between the subjective well-being (SWB) and the subjective and objective measures of the individual material situation in the four post-communist countries of Central Europe (the Czech Republic, Hungary, Poland, and Slovakia). The material situation is measured by income, relative income compared to others, relative income compared to one’s own past, perceived economic strain, financial problems, material deprivation, and housing problems. Our analysis is based on empirical data from the third wave of European Quality of Life Study conducted in 2011. Bayesian networks as a graphical representation of the relations between SWB and the material situation have been constructed in five versions. The models have been assessed using the Bayesian Information Criterion (BIC) and SWB prediction accuracy, and compared\nwith Ordinal Logistic Regression (OLR). Expert knowledge, as well as three different algorithms (greedy, Gobnilp, and Tree-augmented Naive Bayes) were used for learning the network structures. Network parameters were learned using the EM algorithm. Parameters based on OLR were learned for a version of the expert model. The Gobnilp model, the Markov equivalent to the greedy model, is BIC optimal. The OLR predicts SWB slightly better than the other models. We conclude that the objective material conditions' influence on SWB is rather indirect, through the subjective situational assessment of various aspects related to the individual material conditions. Keywords: Subjective Well-Being; Income; Economic Strain; Material Deprivation; Bayesian Networks; Central Europe Fulltext is available at external website.
Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach

The objective of this paper is to explore the associations between the subjective well-being (SWB) and the subjective and objective measures of the individual material situation in the four ...

Švorc, Jan; Vomlel, Jiří
Ústav teorie informace a automatizace, 2020

ECB monetary policy and commodity prices
Aliyev, S.; Kočenda, Evžen
2020 - English
We analyze the impact of the ECB monetary policies on global aggregate and sectoral commodity prices using monthly data from January 2001 till August 2019. We employ a SVAR model and assess separately period of conventional monetary policy before global financial crisis (GFC) and unconventional monetary policy during post-crisis period. Our key results indicate that contractionary monetary policy shocks have positive effects on the aggregate and sectoral commodity prices during both conventional and unconvetional monetary policy periods. The effect is statistically significant for aggregate commodity prices during post-crisis period. In terms of sectoral impact, the effect is statistically significant for food prices in both periods and for fuel prices during post-crisis period; other commodities display positive but statistically insignificant responses. Further, we demonstrate that the impact of the ECB monetary policy on commodity prices increased remarkably after the GFC. Our results also suggest that the effect of the ECB monetary policy on commodity prices does not transmit directly through market demand and supply expectations channel, but rather through the exchange rate channel that influences the European market demand directly. Keywords: European Central Bank; commodity prices; monetary policy Fulltext is available at external website.
ECB monetary policy and commodity prices

We analyze the impact of the ECB monetary policies on global aggregate and sectoral commodity prices using monthly data from January 2001 till August 2019. We employ a SVAR model and assess separately ...

Aliyev, S.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
Murray, Sean Ernest; Quinn, Anthony
2020 - English
This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid carcinoma. The work seeks to address some limitations of previous approaches [4] which involve generic, non-selective transfer of archival data. It is proposed that improvements in patient-specific inferences may be achieved via transferring external population knowledge selectively. This involves matching the patient to a similar sub-population based on available metadata, generating a Gaussian Mixture Model within the partitioned data, and optimally transferring a data predictive distribution from the sub-population to the specific patient. Additionally, a performance evaluation method is proposed and early-stage results presented. Keywords: Bayesian estimation; thyroid carcinoma; patient-specific inferences Fulltext is available at external website.
Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy

This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid ...

Murray, Sean Ernest; Quinn, Anthony
Ústav teorie informace a automatizace, 2020

Bayesian transfer learning between autoregressive inference tasks
Barber, Alec; Quinn, Anthony
2020 - English
Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that any requirement for the design or assumption of a full model between target and sources is a restrictive form of transfer learning. Keywords: autoregression; transfer learning; Fully Probabilistic Design; FPD; food-commodities price prediction Fulltext is available at external website.
Bayesian transfer learning between autoregressive inference tasks

Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that ...

Barber, Alec; Quinn, Anthony
Ústav teorie informace a automatizace, 2020

Kombinace prediktorů v odhadování parametrů
Podlesna, Yana; Kárný, Miroslav
2020 - Czech
Tato bakalářská práce se zabývá návrhem metody k řešení prokletí rozměrnosti vznikající v kvantitativním modelování složitých vzájemně propojených systémů. Jedná se o předpovídací modely, které jsou založené na diskrétním markovském rozhodovacím procesu. Předpovídání je založeno na odhadu parametrů modelu pomocí bayesovské statistiky. Tato práce obsahuje návod na zmenšení rozměrnosti dat, potřebných k předpovídání v systémech s velkým počtem stavů a akcí. Místo odhadu prediktoru závislého na všech parametrech metoda předpokládá užití několika prediktorů, které vznikají odhadováním parametrických modelů, předpokládajících závislost na různých regresorech. Vlastnosti chování navržené metody jsou ilustrovány simulačními experimenty. This bachelor thesis deals with the design of the method for solving the curse of dimensionality arising in the quantitative modeling of complex interconnected systems. The employed predictive models are based on a discrete Markov process. Prediction is based on estimating model parameters using Bayesian statistics. This work contains method for reducing the amount of data needed for prediction in systems with a large number of occurring states and actions. Instead of estimating a predictor dependent on all parameters, the method assumes the use of several predictors, which arise from estimating parametric models based on dependences on different regressors. The behavioral properties of the proposed method are illustrated by simulation experiments. Keywords: curse of dimensionality; Bayesian estimation; prediction; Markov decision process; decision making Fulltext is available at external website.
Kombinace prediktorů v odhadování parametrů

Tato bakalářská práce se zabývá návrhem metody k řešení prokletí rozměrnosti vznikající v kvantitativním modelování složitých vzájemně propojených systémů. Jedná se o předpovídací modely, které jsou ...

Podlesna, Yana; Kárný, Miroslav
Ústav teorie informace a automatizace, 2020

Potential Radioactive Hot Spots Induced by Radiation Accident Being Underway of Atypical Low Wind Meteorological Episodes
Pecha, Petr; Tichý, Ondřej; Pechová, E.
2020 - English
Hypothetical radioactivity release with potentially high variability of the source strength is examined. The interactions of the radioactive cloud with surface and atmospheric precipitation are studied and possible adverse consequences on the environment are estimated. The worst-case scenario is devised in two stages starting with a calm meteorological situation succeeded by wind. At the first stage, the discharges of radionuclides into the motionless ambient atmosphere are assumed. During several hours of this calm meteorological situation, a relatively significant level of radioactivity can be accumulated around the source. At the second stage, the calm is assumed to terminate and convective movement of the air immediately starts. The pack of accumulated radioactivity in the form of multiple Gaussian puffs is drifted by wind and pollution is disseminated over the terrain. The results demonstrate the significant transport of radioactivity even behind the protective zone of a nuclear facility (up to between 15 and 20 km). In the case of rain, the aerosols are heavily washed out and dangerous hot spots of the deposited radioactivity can surprisingly emerge even far from the original source of the pollution. Keywords: radioactivity; atmospheric dissemination; deposition hot-spots Fulltext is available at external website.
Potential Radioactive Hot Spots Induced by Radiation Accident Being Underway of Atypical Low Wind Meteorological Episodes

Hypothetical radioactivity release with potentially high variability of the source strength is examined. The interactions of the radioactive cloud with surface and atmospheric precipitation are ...

Pecha, Petr; Tichý, Ondřej; Pechová, E.
Ústav teorie informace a automatizace, 2020

DEnFi: Deep Ensemble Filter for Active Learning
Ulrych, Lukáš; Šmídl, Václav
2020 - English
Deep Ensembles proved to be a one of the most accurate representation of uncertainty for deep neural networks. Their accuracy is beneficial in the task of active learning where unknown samples are selected for labeling based on the uncertainty of their prediction. Underestimation of the predictive uncertainty leads to poor exploration of the method. The main issue of deep ensembles is their computational cost since multiple complex networks have to be computed in parallel. In this paper, we propose to address this issue by taking advantage of the recursive nature of active learning. Specifically, we propose several methods how to generate initial values of an ensemble based of the previous ensemble. We provide comparison of the proposed strategies with existing methods on benchmark problems from Bayesian optimization and active classification. Practical benefits of the approach is demonstrated on example of learning ID of an IoT device from structured data using deep-set based networks. Keywords: Deep Ensembles; uncertainty; neural networks Fulltext is available at external website.
DEnFi: Deep Ensemble Filter for Active Learning

Deep Ensembles proved to be a one of the most accurate representation of uncertainty for deep neural networks. Their accuracy is beneficial in the task of active learning where unknown samples are ...

Ulrych, Lukáš; Šmídl, Václav
Ústav teorie informace a automatizace, 2020

Macroeconomic Responses of Emerging Market Economies to Oil Price Shocks: Analysis by Region and Resource Profile
Togonidze, S.; Kočenda, Evžen
2020 - English
This study employs a vector autoregressive (VAR) model to analyse how oil price shocks affect macroeconomic fundamentals in emerging economies. Findings from existing literature remain inconclusive how macroeconomic variables fare towards shocks, especially in emerging economies. The objective of our study is to uncover if analysis by region (Latin America and the Caribbean, East Asia and the Pacific, Europe, and Central Asia) and resource intensity of economies (oil exporters, oil importers, minerals exporters, and less resource intensive). Our unique approach forms part of our contribution to the literature. We find that Latin America and the Caribbean are least affected by oil price shocks, while in East Asia and the Pacific the response of inflation and interest rate to oil price shocks is positive, and output growth is negative. Our analysis by resource endowment fails to show oil price shocks’ ability to explain huge variations in macroeconomic variables in oil importing economies. Further sensitivity analysis using US interest rates as an alternative source of external shocks to emerging economies establishes a significant response of interest rate responses to US interest rate in Europe and Central Asia, and in inflation in Latin America and the Caribbean. We also find that regardless of resource endowment, the response of output growth and capital to a positive US interest rate shock is negative and significant in EMs. Our results are persuasive that resource intensity and regional factors impact the responsiveness of emerging economies to oil price shocks, thus laying a basis for policy debate.\n Keywords: Emerging market economies; Oil price shocks; Output growth; Panel VAR Fulltext is available at external website.
Macroeconomic Responses of Emerging Market Economies to Oil Price Shocks: Analysis by Region and Resource Profile

This study employs a vector autoregressive (VAR) model to analyse how oil price shocks affect macroeconomic fundamentals in emerging economies. Findings from existing literature remain inconclusive ...

Togonidze, S.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

Algoritmický výběr dosažitelných preferencí
Siváková, Tereza; Kárný, Miroslav
2019 - Czech
Tato bakalářská práce se zabývá teorií optimálního rozhodování pro diskrétní markovský rozhodovací proces z hlediska volby preferencí. Za pomoci plně pravděpodobnostního návrhu, který zavádí tzv. ideální distribuci chování, která přiřazuje vysoké hodnoty pravděpodobnosti preferovanému chování a malé hodnoty pravděpodobnosti nežádoucímu chování, se hledá optimální rozhodovací politika. Tato práce obsahuje návod k nalezení optimální ideální distribuce chování a přináší obecnější řešení než řešení dosud známá. Dále přidává možnost respektování další preference, a to na volbu akcí. Vlastnosti výsledného rozhodování jsou ilustrovány simulačními experimenty. This bachelor’s thesis studies the optimal decision making for a discrete Markov decision process with a focus on preferences. By using a fully probabilistic design that introduces the so-called ideal behavior distribution, which has high probability values of preferred behaviors and small probability values of inappropriate behaviors, an optimal decision policy has been found. The thesis constructs an algorithm for selecting the optimal ideal behavior distribution and provides a more general solution than published ones. The thesis also opens a possibility to specify further preferences on selected actions. Properties of the resulting decision making are illustrated on simulated examples. Keywords: decision-making; probabilistic policies; quantification of aims Fulltext is available at external website.
Algoritmický výběr dosažitelných preferencí

Tato bakalářská práce se zabývá teorií optimálního rozhodování pro diskrétní markovský rozhodovací proces z hlediska volby preferencí. Za pomoci plně pravděpodobnostního návrhu, který zavádí tzv. ...

Siváková, Tereza; Kárný, Miroslav
Ústav teorie informace a automatizace, 2019

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