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Transformation of data in the framework of dynamic decision making
Chudoba, Martin ; Jirsa, Ladislav (advisor) ; Kalina, Jan (referee)
Nazev pram: Transformace finnncnich dat, urcenyeh pro dynamicke rozhodovam Autor: Martin Cliudoba Katedra (ustav): Katedra pra.vdepodolmosti a matcrnaticke statistiky Vedouci bakalafsko prace: UNDr. Ladislav Jirsa, PhD. E-mail vedouciho: jirsaPutia.cas.cz Abst.rakt: V pfcdlo/eiic praci je pfibh'zen problem optimalm'ho rozhodovani pfi bur/oviiim obchoclovani s tzv. ''financial futures", tj. s ternrinovaiiymi finaric.iiimi obchody. rl'ato uloha je prevedena do zjediioduHrnrlio inatcinatif;k{''ho modolu, ktery je fesitolny za pomori metod Bay(5Ko\sk6ho odhadovani. Fitianenf data jsou inodelovana autoregreHiiim modelein a norinalniin sumc'in, jelikoz ji; ji/ vyvinu- ta rada iiastrojii ])rod])C)kladaju'kih pravc normalni KUIII, kt.erc slou/i k prcdikci vyvoje c-eny na trim. Illaviiim eileiii letcj ]jrace je ])orovnavani vyhodnosti riiznych transforniacf vstupnicli dat tak, al>y jojich sum mcl iionualuf rozdelem' a tudi'z aby predikce ceny byla. eo iiejijfesnejsi. Pfislusuy algoritiuus je uaprogramoYan v jazyce Mat.lab; prezeiita.ee dosazenyt'h vysledku tvoff zaverefniou f:ast teto praco. Klfrova slo\'a: Hayesovskr odhadovanf, finanm., trausformare dat Title: Transforniation of data, in the franunvork of dynamic1, decision making Author: Martin Cliudoba Department: Department of Probability and Mathematieal...
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Transformation of data in the framework of dynamic decision making
Chudoba, Martin ; Kalina, Jan (referee) ; Jirsa, Ladislav (advisor)
Nazev pram: Transformace finnncnich dat, urcenyeh pro dynamicke rozhodovam Autor: Martin Cliudoba Katedra (ustav): Katedra pra.vdepodolmosti a matcrnaticke statistiky Vedouci bakalafsko prace: UNDr. Ladislav Jirsa, PhD. E-mail vedouciho: jirsaPutia.cas.cz Abst.rakt: V pfcdlo/eiic praci je pfibh'zen problem optimalm'ho rozhodovani pfi bur/oviiim obchoclovani s tzv. ''financial futures", tj. s ternrinovaiiymi finaric.iiimi obchody. rl'ato uloha je prevedena do zjediioduHrnrlio inatcinatif;k{''ho modolu, ktery je fesitolny za pomori metod Bay(5Ko\sk6ho odhadovani. Fitianenf data jsou inodelovana autoregreHiiim modelein a norinalniin sumc'in, jelikoz ji; ji/ vyvinu- ta rada iiastrojii ])rod])C)kladaju'kih pravc normalni KUIII, kt.erc slou/i k prcdikci vyvoje c-eny na trim. Illaviiim eileiii letcj ]jrace je ])orovnavani vyhodnosti riiznych transforniacf vstupnicli dat tak, al>y jojich sum mcl iionualuf rozdelem' a tudi'z aby predikce ceny byla. eo iiejijfesnejsi. Pfislusuy algoritiuus je uaprogramoYan v jazyce Mat.lab; prezeiita.ee dosazenyt'h vysledku tvoff zaverefniou f:ast teto praco. Klfrova slo\'a: Hayesovskr odhadovanf, finanm., trausformare dat Title: Transforniation of data, in the franunvork of dynamic1, decision making Author: Martin Cliudoba Department: Department of Probability and Mathematieal...
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Normal and uniform noise - violation of the assumption on noise distribution in model identification
Jirsa, Ladislav ; Pavelková, Lenka
Mathematical modelling under uncertainty together with the field of applied statistics represent tools useful in many practical domains. Widely accepted assumption of normal (Gaussian) noise has created the basis for theoretical and algorithmic solutions of respective tasks. However, many continuous variables are strictly bounded and their uncertainty may have origin in various physical processes which causes a non-normal distribution of their noise. Furthermore, adaptation of algorithms based on normal model for identification of models with bounded noise can distort the estimates due to inconsistent handling of uncertainty. This report describes a study to compare results of estimation algorithms based on assumption of normal and uniform noise. Data sequences processed by the algorithms have normal noise bounded by a low limit with respect to standard deviation. We illustrate disparity between noise assumption and a true noise distribution and its influence on the quality of the estimates. It is a part of an effort to develop theory and fast algorithms for estimation with bounded noise, applicable in practice.
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Preliminaries of probabilistic hierarchical fault detection
Jirsa, Ladislav ; Pavelková, Lenka ; Dedecius, Kamil
The paper proposes a novel probabilistic fault detection and isolation (FDI) system that enables to evaluate dynamically the industrial system condition (health) at any level of its functional hierarchy. The investigated industrial system is considered as a set of interconnected individual components. Each component acts in its noisy environment as an imperfect participant, more or less dependent on neighbouring components and, in turn, influencing some others. The nature of the problem prevents us from expressing sufficiently hard propositions about the health of the system as a whole at once but we can observe and construct propositions at lower system hierarchies. These propositions (opinions) are combined at higher levels using the rules of probabilistic logic, retaining the ignorance and finally yielding a single opinion on the health of the whole monitored system.
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Impact of forgetting on models of rolling mills
Dedecius, Kamil ; Jirsa, Ladislav
The research report deals with an analysis of various models for modelling of the cold sheet rolling process. It comprises a thorough analysis of a mass-flow model and its weaknesses, brief analysis of normalization impact on modelling and exhaustive analysis of 4 defined models with exponential and partial forgetting and their comparison to models without forgetting. The report ends with a computer-intensive search for new blackbox models.
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Iterativní formulace cílů řízení v plně pravděpodobnostním návrhu
Jirsa, Ladislav ; Kárný, Miroslav ; Tesař, Ludvík
A control design converts knowledge about the controlled system, constraints and control aims into the controller. Control aims must be quantified in the way compatible to the design. A systematic quantification of the control aims, called aim elicitation, is the least supported step of the design process. We present a solution of this problem within the framework of a fully probabilistic design (FPD) [1]. Any controller modifies the closed-loop behaviour to reach the control aims. The controller is chosen in order to minimize a given loss function. The FPD selects the controller that minimizes the Kullback- Leibler divergence of the joint probability density function (pdf) describing closed-loop behaviour to the ideal pdf. The ideal pdf expresses both the desired closed-loop behaviour and constraints on system inputs. Thus within the FPD, the aim elicitation reduces to the choice of the ideal pdf. For complex multidimensional systems, the task to construct the ideal pdf may represent a nontrivial problem requiring an expert experienced both in practical treatment of the system and theory as well.
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Transformace finančních dat určených pro dynamické rozhodování
Chudoba, M. ; Jirsa, Ladislav
In the presented work we are introduced to the problem of optimal decision making while dealing on the exchange with so-called "financial futures", i.e. time financial transaction. This task is transferred into the simplified mathematical model, which is solvable using Bayesian estimation methods. Financial data are modelled by auto-regressive model with normal noise, because the tools, which are exploited for prediction of the price on the market and which assume normal noise, have been already developed. The main goal of this work is the comparison of the efficiency of various transformations on input data, so that their noise had normal distribution, therefore the price prediction was as accurate as possible. The applicable algorithm is programmed in Matlab; the presentation of achieved results forms the final part of this thesis.
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Identifikace aktivity štítné žlázy a pravděpodobnostní odhadování absorbovaných dávek v nukleární medicíně
Jirsa, Ladislav ; Quinn, A. ; Varga, F.
The Bayesian identification of a linear regression model (called the biphasic model) for time dependence of thyroid gland activity in 131I radiotherapy is presented. Prior knowledge is elicited via hard parameter constraints and via the merging of external information from an archive of patient records. This prior regularization is shown to be crucial in the reported context, where data typically comprise only two or three high-noise measurements. The posterior distribution is simulated via a Langevin diffusion algorithm, whose optimization for the thyroid activity application is explained. Excellent patient-specific predictions of thyroid activity are reported. The posterior inference of the patient-specific total radiation dose is computed, allowing the uncertainty of the dose to be quantified in a consistent form. The relevance of this work in clinical practice is explained.
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