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Wyszukujesz frazę "Bayesian estimation" wg kryterium: Temat


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Tytuł:
Wykorzystanie sieci bayesowskich w szacowaniu ryzyka innowacyjnego
Using bayesian networks to estimate the innovative risk
Autorzy:
Knosala, R.
Landwójtowicz, A
Powiązania:
https://bibliotekanauki.pl/articles/340109.pdf
Data publikacji:
2013
Wydawca:
Polskie Towarzystwo Zarządzania Produkcją
Tematy:
innowacje
ryzyko innowacyjne
szacowanie ryzyka
innovations
innovative risk
risk estimation
Bayesian networks
Opis:
Today, the advantage of enterprises is built by the process of innovations implementation. A decision concerning the innovations implementation is always difficult and risky because innovations are specific kinds of investments and are a potential source of many threats. This is why before taking a decision about an implementation of a given solution, it is extremely important to make an analysis of its consequences. A risk analysis becomes more and more important in this aspect because it makes it possible to estimate the level of dangers which can be caused by a new investment solution. This is why the process of estimating innovation risk with the use of Bayesian networks has been presented in this work. Data from projects carried out under the Operational Programme Innovative Economy for the years 2007-2013 in Opole Province and the NETICA programme have been used in order to work out an exemplary method. It has been shown how to determine the innovative risk level with taking into consideration the adopted assumptions. Exemplary factors of the analysed risk concerning both the enterprise and the sheer undertaking have been characterised. In the first step, the most important factors of innovation risk and their measuring indicators have been specified. Assuming that the risk is a probability of an undesirable state occurrence (according to a negative concept), the authors have chosen the following indicators to estimate the danger of an innovation failure: W 1. Period of using technology in the world. W 2. Time of carrying out the project expressed in months. W 3. Value of the whole project. W 4. Size of the enterprise. W 5. Own financial resources designed for making innovation. W 6. Financial risk. W 7. Decision about granting a subsidy. The chosen factors (sources) of risk are only an exemplary set and were chosen on purpose from the point of view of an area of the analysed risk. It is necessary to remember that each potential source of danger can become the basis of a subsequent risk connected with the project being carried out. In this context, an aspect of choosing appropriate and the most important risk sources, from the point of view of the innovation efficacy, appears. It is an extremely important stage because as we know it is impossible to take into consideration all factors because the assessment of accuracy of the estimated risk shall depend on it. In this case authors also highlight the role of an expert who mainly directs the risk estimation process. This step is a little subjective but in reality, the subjectivity is present in almost every step of risk analysis. The next step included the specification of dependencies between the enumerated factors and the probability of the analysed states occurrence. Thanks to that, the elaboration of a simple Bayesian network has become possible. It has been shown, on its basis, how the level of innovation risk an be estimated if the specific information and assumptions are available.
Źródło:
Zarządzanie Przedsiębiorstwem; 2013, 16, 1; 28-34
1643-4773
Pojawia się w:
Zarządzanie Przedsiębiorstwem
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary computation based on Bayesian classifiers
Autorzy:
Miquelez, T.
Bengoetxea, E.
Larranaga, P.
Powiązania:
https://bibliotekanauki.pl/articles/907630.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
rozumowanie probabilistyczne
obliczenia ewolucyjne
sieć Bayesa
estymacja algorytmu dystrybucji
hybrid soft computing
probabilistic reasoning
evolutionary computing
classification
optimization
Bayesian networks
estimation of distribution algorithms
Opis:
Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier---either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one---is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 335-349
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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