Number of found documents: 863
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Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains
Sladký, Karel
2018 - English
In this note we consider continuous-time Markov decision processes with finite state and actions spaces where the stream of rewards generated by the Markov processes is evaluated by an exponential utility function with a given risk sensitivitycoefficient (so-called risk-sensitive models). If the risk sensitivity coefficient equals zero (risk-neutral case) we arrive at a standard Markov decision process. Then we can easily obtain necessary and sufficient mean reward optimality conditions and the variability can be evaluated by the mean variance of total expected rewards. For the risk-sensitive case, i.e. if the risk-sensitivity coefficient is non-zero, for a given value of the risk-sensitivity coefficient we establish necessary and sufficient optimality conditions for maximal (or minimal) growth rate of expectation of the exponential utility function, along with mean value of the corresponding certainty equivalent. Recall that in this case along with the total reward also its higher moments are taken into account. Keywords: continuous-time Markov decision chains; exponential utility functions; certainty equivalent; mean-variance optimality; connections between risk-sensitive and risk-neutral optimality Fulltext is available at external website.
Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains

In this note we consider continuous-time Markov decision processes with finite state and actions spaces where the stream of rewards generated by the Markov processes is evaluated by an exponential ...

Sladký, Karel
Ústav teorie informace a automatizace, 2018

Internet věcí v praxi
Zajíček, Milan
2018 - Czech
Předpokladem práce s mikrokontrolery je pochopení základních principů jejich komunikace s okolím a možné způsoby jejich programování. Mikrokontroler ESP8266 je jedním z nejznámějších obvodů používaných v aplikacích pro internet věcí. Jednou z vývojových platforem, která se o ESP8266 opírá, je modulární systém Wemos D1 mini, který umožňuje přímou komunikaci s PC prostřednictvím USB portu a disponuje dostatkem vstupních a výstupních periférií pro stavbu jednoduchých IoT zařízení. Pro programování je použito vývojové prostředí ArduinoIde. Tento tutorial provede úplného začátečníka od instalace software až k sestavení termostatu pro spínání silové zátěže. Prerequisite of working with microcontrollers is understanding the basic principles of their communication with the environment and possible ways of their programming. The ESP8266 microcontroller is one of the most popular chips used in Internet of things applications. Wemos D1 mini is one of development platforms behind the ESP8266,which allows direct communication with the PC via a USB port and has plenty of input and output peripherals to build simple IoT devices. The ArduinoIde development environment is used for programming. The thermostatic switch is made as a result of this tutorial. Keywords: microcontrollers; Internet of things; ESP8266 Fulltext is available at external website.
Internet věcí v praxi

Předpokladem práce s mikrokontrolery je pochopení základních principů jejich komunikace s okolím a možné způsoby jejich programování. Mikrokontroler ESP8266 je jedním z nejznámějších obvodů ...

Zajíček, Milan
Ústav teorie informace a automatizace, 2018

Multi-Objective Optimization Problems with Random Elements - Survey of Approaches
Kaňková, Vlasta
2018 - English
Many economic and financial situations depend simultaneously on a random element and a decision parameter. Mostly, it is possible to influence the above mentioned situation only by an optimization model depending on a probability measure. This optimization problem can be static (one-stage), dynamic with finite or infinite horizon, single-objective or multi-objective. We focus on one-stage multi-objective problems corresponding to applications those are suitable to evaluate simultaneously by a few objectives. The aim of the contribution is to give a survey of different approaches (as they are known from the literature) of the above mentioned applications. To this end we start with well-known mean-risk model and continue with other known approaches. Moreover, we try to complete every model by a suitable application. Except an analysis of a choice of the objective functions type we try to discuss suitable constraints set with respect to the problem base, possible investigation and relaxation. At the end we mention properties of the problem in the case when the theoretical „underlying“ probability measure is replaced by its „deterministic“ or „stochastic“ estimate. Keywords: multi-objective optimization problems; random element; mean-risk model; deterministic approach; stochastic multi-objective problems; constraints set; relaxation Fulltext is available at external website.
Multi-Objective Optimization Problems with Random Elements - Survey of Approaches

Many economic and financial situations depend simultaneously on a random element and a decision parameter. Mostly, it is possible to influence the above mentioned situation only by an optimization ...

Kaňková, Vlasta
Ústav teorie informace a automatizace, 2018

On attempts to characterize facet-defining inequalities of the cone of exact games
Studený, Milan; Kroupa, Tomáš; Kratochvíl, Václav
2018 - English
The sets of balanced, totally balanced, exact and supermodular games play an important role in cooperative game theory. These sets of games are known to be polyhedral cones. The (unique) non-redundant description of these cones by means of the so-called facet-defining inequalities is known in cases of balanced games and supermodular games, respectively. The facet description of the cones of exact games and totally balanced games are not known and we present conjectures about what are the facet-defining inequalities for these cones. We introduce the concept of an irreducible min-balanced set system and conjecture that the facet-defining inequalities for the cone of totally balanced games correspond to these set systems. The conjecture concerning exact games is that the facet-defining inequalities for this cone are those which correspond to irreducible min-balanced systems on strict subsets of the set of players and their conjugate inequalities. A consequence of the validity of the conjectures would be a novel result saying that a game m is exact if and only if m and its reflection are totally balanced. Keywords: exact game; extremity; irreducible; balanced Fulltext is available at external website.
On attempts to characterize facet-defining inequalities of the cone of exact games

The sets of balanced, totally balanced, exact and supermodular games play an important role in cooperative game theory. These sets of games are known to be polyhedral cones. The (unique) non-redundant ...

Studený, Milan; Kroupa, Tomáš; Kratochvíl, Václav
Ústav teorie informace a automatizace, 2018

Solution of Emission Management Problem
Šmíd, Martin; Kozmík, Václav
2018 - English
Optimal covering of emissions stemming from random production is a multistage stochastic programming problem. Solving it in a usual way - by means of deterministic equivalent - is possible only given an unrealistic approximation of random parameters. There exists an efficient way of solving multistage problems - stochastic dual dynamic programming (SDDP), however, it requires the inter-stage independence of random parameters, which is not the case which our problem. In the paper, we discuss a modified version of SDDP, allowing for some form of interstage dependence. Keywords: Multi-stage stochastic programming; Emission management; SDDP; time dependence Fulltext is available at external website.
Solution of Emission Management Problem

Optimal covering of emissions stemming from random production is a multistage stochastic programming problem. Solving it in a usual way - by means of deterministic equivalent - is possible only given ...

Šmíd, Martin; Kozmík, Václav
Ústav teorie informace a automatizace, 2018

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

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