Number of found documents: 539
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Determinants of Financial Inclusion in Africa and OECD Countries
Kočenda, Evžen; Eshun, S. F.
2023 - English
Sub-Saharan Africa (SSA) has been identified as one of the least financially inclusive regions in the world with a huge disparity in comparison to highly financially inclusive regions. Using a dynamic panel data analysis, we explore the factors influencing financial inclusion in Sub-Saharan Africa (SSA) using countries belonging to the Organisation for Economic Co-operation and Development (OECD) as a benchmark. We employ the System Generalized Method of Moments (GMM) estimator and assess 31 SSA and 38 OECD countries from 2000-2021. We show that the differences in trade openness, banks' efficiency, income, and remittances are some macro-level factors that explain the variation in financial inclusion levels. We highlight the importance of quality literacy policies, trade improvement with restrictions on cross-border capital flows, and a more efficient financial system to promote financial inclusion. Keywords: Financial Inclusion; Financial Inclusion Index; Sub-Saharan Africa Fulltext is available at external website.
Determinants of Financial Inclusion in Africa and OECD Countries

Sub-Saharan Africa (SSA) has been identified as one of the least financially inclusive regions in the world with a huge disparity in comparison to highly financially inclusive regions. Using a dynamic ...

Kočenda, Evžen; Eshun, S. F.
Ústav teorie informace a automatizace, 2023

Average Reward Optimality in Semi-Markov Decision Processes with Costly Interventions
Sladký, Karel
2023 - English
In this note we consider semi-Markov reward decision processes evolving on finite state spaces. We focus attention on average reward models, i.e. we establish explicit formulas for the growth rate of the total expected reward. In contrast to the standard models we assume that the decision maker can also change the running process by some (costly) intervention. Recall that the result for optimality criteria for the classical Markov decision chains in discrete and continuous time setting turn out to be a very specific case of the considered model. The aim is to formulate optimality conditions for semi-Markov models with interventions and present algorithmical procedures for finding optimal solutions. Keywords: controlled semi-Markov reward processes; long-run optimality; intervention of the decision maker Fulltext is available at external website.
Average Reward Optimality in Semi-Markov Decision Processes with Costly Interventions

In this note we consider semi-Markov reward decision processes evolving on finite state spaces. We focus attention on average reward models, i.e. we establish explicit formulas for the growth rate of ...

Sladký, Karel
Ústav teorie informace a automatizace, 2023

Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks
Kratochvíl, F.; Kratochvíl, Václav; Saad, G.; Vomlel, Jiří
2022 - English
A loanword is a word permanently adopted from one language and incorporated into another language without translation. In this paper, we study loanwords in the South-East Asia Archipelago, home to a large number of languages. Our paper is inspired by the works of Hoffmann et al. (2021) Bayesian methods are applied to probabilistic modeling of family trees representing the history of language families and by Haynie et al. (2014) modeling the diffusion of a special class of loanwords, so-called Wanderw ̈orter in languages of Australia, North America, and South America. We assume that in the South-East Asia Archipelago Wanderwörter spread along specific maritime trade routes whose geographical characteristics can help unravel the history of Wanderwörter diffusion in the area. For millennia trade was conducted using sailing ships which were constrained by the monsoon system and in certain areas also by strong sea currents. Therefore rather than the geographical distances, the travel times of sailing ships should be considered as a major factor determining the intensity of contact among cultures. We use sailing navigation software to estimate travel times between different ports and show that the estimated travel times correspond well to the travel times of a Chinese map of the sea trade routes from the early seventeenth century. We model the spread of loanwords using a probabilistic graphical model - a Bayesian network. We design a novel heuristic Bayesian network structure learning algorithm that learns the structure as a union of spanning trees for graphs of all loanwords in the training dataset. We compare this algorithm with BIC optimal Bayesian networks by measuring how well these models predict the true presence/absence of a loanword. Interestingly, Bayesian networks learned by our heuristic spanning tree-based algorithm provide better results than the BIC optimal Bayesian networks. Keywords: loanwords; Bayesian methods; probabilistic graphical model Fulltext is available at external website.
Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks

A loanword is a word permanently adopted from one language and incorporated into another language without translation. In this paper, we study loanwords in the South-East Asia Archipelago, home to a ...

Kratochvíl, F.; Kratochvíl, Václav; Saad, G.; Vomlel, Jiří
Ústav teorie informace a automatizace, 2022

A Bootstrap Comparison of Robust Regression Estimators
Kalina, Jan; Janáček, Patrik
2022 - English
The ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more preferable alternatives. Keywords: linear regression; robust estimation; nonparametric bootstrap; bootstrap hypothesis testing Fulltext is available at external website.
A Bootstrap Comparison of Robust Regression Estimators

The ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more ...

Kalina, Jan; Janáček, Patrik
Ústav teorie informace a automatizace, 2022

Jak to v těch datech najít?
Zitová, Barbara; Šorel, Michal
2022 - Czech
Přednáška si klade za cíl seznámit odbornou veřejnost s aktivitami oddělení Zpracování obrazové informace ÚTIA AV ČR v.v.i. v oblasti analýz dat projektu Copernicus. Oddělení se dlouhodobě zabývá vývojem metod digitálního zpracování obrazu a hlubokého učení. Během posledních dvou let vzniklo několik demonstračních studentských prací ve spolupráci s MFF UK a FJFI ČVUT využívajících data z družic Sentinel, jako například rozpoznávání typů plodin z časových řad snímků ze satelitu Sentinel-2, automatická segmentace oblastí podle způsobu využití či typu povrchu pomocí metod strojového učení, přesnější detekce mraků v datech ze Sentinel-2, ve spolupráci s Ústavem pro hydrodynamiku AV ČR postupy pro odhad vlhkosti povrchové vrstvy krajiny z dat družice Sentinel-2 a zvyšování rozlišení tepelných dat Sentinel-3 pomocí metod hlubokého učení. V druhé části budou přiblíženy možnosti aplikace metod vyvinutých pro jiné oblasti (separace zdrojů \ninformace) v DPZ. The lecture aims to introduce the activities of the Image Processing Department of the Institute of Image Information of the CAS in the field of Copernicus data analysis to the professional public. The department has long been involved in the development of digital image processing and deep learning methods. During the last two years, in cooperation with the MFF UK and FJFI CTU, several student demonstration projects using data from Sentinel satellites have been finished, such as crop type recognition from Sentinel-2 time-series images, automatic segmentation of areas by land use or surface type using machine learning methods learning, more accurate cloud detection in Sentinel-2 data, in collaboration with the Institute of Hydrodynamics of the CAS Czech Republic, procedures for estimating landscape surface moisture from Sentinel-2 data and increasing the resolution of Sentinel-3 thermal data using deep learning methods. The second part will present the application of developed methods for other areas in remote sensing. Keywords: remote sensing; satellite imaging; machine learning Fulltext is available at external website.
Jak to v těch datech najít?

Přednáška si klade za cíl seznámit odbornou veřejnost s aktivitami oddělení Zpracování obrazové informace ÚTIA AV ČR v.v.i. v oblasti analýz dat projektu Copernicus. Oddělení se dlouhodobě zabývá ...

Zitová, Barbara; Šorel, Michal
Ústav teorie informace a automatizace, 2022

Two Composition Operators for Belief Functions Revisited
Jiroušek, Radim; Kratochvíl, Václav; Shenoy, P. P.
2022 - English
In probability theory, compositional models are as powerful as Bayesian networks. However, the relation between belief-function graphical models and the corresponding compositional models is much more complicated due to several reasons. One of them is that there are two composition operators for belief functions. This paper deals with their main properties and presents sufficient conditions under which they yield the same results. Keywords: probability theory; Bayesian networks; belief functions Fulltext is available at external website.
Two Composition Operators for Belief Functions Revisited

In probability theory, compositional models are as powerful as Bayesian networks. However, the relation between belief-function graphical models and the corresponding compositional models is much more ...

Jiroušek, Radim; Kratochvíl, Václav; Shenoy, P. P.
Ústav teorie informace a automatizace, 2022

Modeling COVID Pandemics: Strengths and Weaknesses of Epidemic Models
Šmíd, Martin
2022 - English
We generally discuss modeling the present COVID pandemics. We argue that useful models have to be simple in the first case, yet their uncertainty has to be handled properly. In order to study circumstances of the upcoming wave of infection,\nwe construct a simple stochastic model and present predictions it gives. We conclude that the autumn wave is most likely unavoidable and suggest concentrating to mitigation. Keywords: COVID; Epidemic Models; stochastic model Fulltext is available at external website.
Modeling COVID Pandemics: Strengths and Weaknesses of Epidemic Models

We generally discuss modeling the present COVID pandemics. We argue that useful models have to be simple in the first case, yet their uncertainty has to be handled properly. In order to study ...

Šmíd, Martin
Ústav teorie informace a automatizace, 2022

Analysis of Impact of Covariates Entering Stochastic Optimization Problem
Volf, Petr
2022 - English
In the contribution we study consequences of imperfect information to precision of stochastic optimization solution. In particular, it is assumed that the characteristics of optimization problem are influenced by a set of covariates. This dependence is described via a regression model. Hence, the uncertainty is then caused by statistical estimation of regression parameters. The contribution will analyze several regression model cases, together with their application. Precision of results will be explored, both theoretically as well as with the aid of simulations. Keywords: stochastic optimization; regression model; statistical estimation Fulltext is available at external website.
Analysis of Impact of Covariates Entering Stochastic Optimization Problem

In the contribution we study consequences of imperfect information to precision of stochastic optimization solution. In particular, it is assumed that the characteristics of optimization problem are ...

Volf, Petr
Ústav teorie informace a automatizace, 2022

Hybrid evaluation of the industrial global impact on Mexican aquifers under uncertain criteria evaluations
Flores Casamayor, H.; Carpitella, Silvia; Izquierdo, J.; Mora-Rodríguez, J.; Delgado-Galván, X.
2022 - English
The present paper proposes an integrated methodological approach to address the problem of managing five aquifers of Guanajuato state, Mexico, according to such relevant criteria as environmental, social, economic and hydrological aspects. The goal of this research consists in formalizing a structured framework to first evaluate the various degrees of importance of criteria and to secondly get a classification of aquifers by minimizing uncertainty of evaluations. To such an aim, the Analytic Hierarchy Process (AHP) is used for calculating the vector of criteria weights, while the Fuzzy Logic (FL) theory supports in deriving quantitative evaluations of aquifers under each selected criterion. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then proposed to formalize the final ranking of aquifers, something that will be helpful to understand which alternative matches all the differently weighted criteria in the most suitable way at a practical level. In such a way, getting a comprehensive and strategic overview about the problem of interest will be possible. Keywords: Analytic Hierarchy Process (AHP); Fuzzy Logic (FL) theory; Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Fulltext is available at external website.
Hybrid evaluation of the industrial global impact on Mexican aquifers under uncertain criteria evaluations

The present paper proposes an integrated methodological approach to address the problem of managing five aquifers of Guanajuato state, Mexico, according to such relevant criteria as environmental, ...

Flores Casamayor, H.; Carpitella, Silvia; Izquierdo, J.; Mora-Rodríguez, J.; Delgado-Galván, X.
Ústav teorie informace a automatizace, 2022

Diffusion Kalman filtering under unknown process and measurement noise covariance matrices
Vlk, T.; Dedecius, Kamil
2022 - English
The state-of-the-art algorithms for Kalman filtering in agent networks with information diffusion impose the requirement of well-defined state-space models. In particular, they assume that both the process and measurement noise covariance matrices are known and properly set. This is a relatively strong assumption in the signal processing domain. By design, the Kalman filters are rather sensitive to its violation, which may potentially lead to their divergence. In this paper, we propose a novel distributed filtering algorithm with increased robustness under unknown process and measurement noise covariance matrices. It is formulated as a Bayesian variational message passing procedure for simultaneous analytically tractable inference of states and measurement noise covariance matrices. Keywords: Collaborative estimation; State estimation; Variational Bayesian methods Fulltext is available at external website.
Diffusion Kalman filtering under unknown process and measurement noise covariance matrices

The state-of-the-art algorithms for Kalman filtering in agent networks with information diffusion impose the requirement of well-defined state-space models. In particular, they assume that both the ...

Vlk, T.; Dedecius, Kamil
Ústav teorie informace a automatizace, 2022

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