Relevant bibliographies by topics / Metoda Monte Carlo

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Metoda Monte Carlo'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Metoda Monte Carlo"

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Matuszak, Natalia. "Monte Carlo jako jedna z metod symulacyjnych w radioterapii." Letters in Oncology Science 16, no. 2 (June 10, 2019): 15–22. http://dx.doi.org/10.21641/los.2019.17.2.91.

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Obecnie fizyka jądrowa coraz częściej stwarza możliwości ku nowym rozwiązaniom w radioterapii. Celem udoskonalenia już istniejących metod jest poszukiwanie bardziej precyzyjnych technologii dających możliwie jak najmniejsze ryzyko błędu. Fizyczne planowanie eksperymentów nierzadko wiąże się z ograniczeniami technicznymi, dlatego dobrym rozwiązaniem staje się modelowanie komputerowe. Do celów radioterapii najczęściej wymienianą metodą jest tzw. metoda Monte Carlo.Istotą tej metody jest symulacja komputerowa procesów o charakterze losowym. W oparciu o nią, algorytm wykorzystuje obliczenia numeryczne do opisu wielkości fizycznych. Stanowi to alternatywę dla procesów zbyt złożonych, dla których podejście analityczne jest niewystarczające by osiągnąć zamierzone cele. Spośród różnych kodów bazujących na obliczeniach Monte Carlo (MCNP, MCNPX, FLUKA, EGSnrc), w radioterapii największe zastosowanie znajduje GEANT4.
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Zemke, Jerzy. "Metoda Monte Carlo w ocenie ryzyka finansowego inwestycji." Optimum Studia Ekonomiczne, no. 3(87) (2017): 48–60. http://dx.doi.org/10.15290/ose.2017.03.87.04.

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SIKORSKI, ANDRZEJ. "Method of Monte Carlo entropy sampling in polymer SYSTEMS." Polimery 45, no. 07/08 (July 2000): 514–19. http://dx.doi.org/10.14314/polimery.2000.514.

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Gustyana, Tieka Trikartika, and Andrieta Shintia Dewi. "ANALISIS PERBANDINGAN KEAKURATAN HARGA CALL OPTION DENGAN MENGGUNAKAN METODE MONTE CARLO SIMULATION DAN METODE BLACK SCHOLES PADA INDEKS HARGA SAHAM GABUNGAN (IHSG)." Jurnal Manajemen Indonesia 14, no. 3 (April 3, 2017): 259. http://dx.doi.org/10.25124/jmi.v14i3.387.

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Opsi adalah salah satu instrument derivative. Option merupakan investasi yang cukup menarik untuk dilakukan apabila volatilitasnya tinggi. Risiko dapat digambarkan dengan volatilitas. Volatilitas menggambarkan probabilitas yang terjadi pada harga saham dari waktu ke waktu. IHSG merupakan Indeks Harga Saham Gabungan yang menggambarkan harga saham di Bursa Efek Indonesia (BEI), dimana IHSG juga merupakan indikator pergerakan harga seluruh saham di BEI.Metode penelitian yang digunakan dalam penelitian ini adalah metode deskriptif. Penentuan harga premi opsi call dengan menggunakan dua metoda yaitu black scholes dan simulais monte carlo. Data yang digunakan adalah data indeks harga saham gabungan (IHSG), dengan penentuan periode waktu jatuh tempo call option 2 bulan dan Agustus 2011 sampai dengan Agustus 2013. Berdasarkan dari hasil penelitian dengan mempergunakan Nilai price absolute error dari dua Metode yaitu Black Scholes dan Monte Carlo dengan jangka waktu jatuh tempo 2 bulan yaitu untuk Metode Black Scholes sebesar 0.02%, sedangkan nilai price absolute error untuk Metode Simulasi Monte Carlo sebesar 2.55%. Berdasarkan nilai price absolute error dengan jangka waktu jatuh tempo 2 bulan, Metode Simulasi Black Scholes memiliki nilai price absolute error yang lebih kecil dibandingkan dengan Metode Monte Carlo, maka dapat disimpulkan Metode Simulasi Black Scholes lebih akurat dibandingkan Metode Monte Carlo.
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Bolibok, Leszek. "The use of Monte Carlo method in significance tests of Ripley's function outcome or how to avoid false discovery of nonrandom spatial structure of tree stand." Forest Research Papers 70, no. 1 (March 1, 2009): 59–67. http://dx.doi.org/10.2478/v10111-009-0006-1.

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Kubisa, Stefan, and Zygmunt Warsza. "Identification of the Capacitor Model Parameters by two Monte Carlo Methods." Pomiary Automatyka Robotyka 22, no. 2 (June 30, 2018): 41–48. http://dx.doi.org/10.14313/par_228/41.

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The Lam, Nguyen. "QUANTUM DIFFUSION MONTE CARLO METHOD FOR LOW-DIMENTIONAL SYSTEMS." Journal of Science, Natural Science 60, no. 7 (2015): 81–87. http://dx.doi.org/10.18173/2354-1059.2015-0036.

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Tomczyk, Krzysztof. "Application of the Monte Carlo Method for Parametric Identification of Accelerometers in the Frequency Domain." Pomiary Automatyka Robotyka 24, no. 2 (June 30, 2020): 31–38. http://dx.doi.org/10.14313/par_236/31.

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Kandidov, V. P. "Monte Carlo method in nonlinear statistical optics." Uspekhi Fizicheskih Nauk 166, no. 12 (1996): 1309. http://dx.doi.org/10.3367/ufnr.0166.199612c.1309.

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Pawlak, Marcin. "The Classic Methods of Real Option Valuation Vs Double Monte Carlo Simulation – Assumptions Analysis." Zeszyty Naukowe Uniwersytetu Szczecińskiego Finanse Rynki Finansowe Ubezpieczenia 82 (2016): 437–45. http://dx.doi.org/10.18276/frfu.2016.4.82/1-37.

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Dissertations / Theses on the topic "Metoda Monte Carlo"

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Pietrzak, Robert. "Modelowanie terapeutycznych wiązek promieniowania za pomocą metody Monte Carlo." Doctoral thesis, Katowice: Uniwersytet Śląski, 2017. http://hdl.handle.net/20.500.12128/5768.

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In this work models of two therapeutic beams of high-energy X-rays and protons were elaborated. The purpose of the investigation was the determination of significant parameters used in clinical dosimetry, basing on the elaborated models. The calculations were performed by means of computer simulations on the ground of the Monte Carlo algorithm. At the first stage of this work the head model of the Clinac 2300 linear accelerator by Varian was elaborated with the use of the professional MCNPX code. Verification of this model was carried out using the comparison between the calculated and measured depth-dose distributions in the standard dosimetry medium – water. The depth-dose distributions in water were determined for SSD = 100 cm and for three square radiation fields of 3 cm x 3 cm, 10 cm x 10 cm and 40 cm x 40 cm. Successful validation of the prepared model with the measuring system model made it possible to begin the next part of this investigation in which the energy spectra along the central axis of the beam and in the direction perpendicular to this axis in water were calculated. The spectral calculations were performed for the therapeutic 20 MV X-ray beam. The influence of the radiation field sizes on the shapes of the spectra in water were determined and the mean energy of the beam was calculated for the chosen irradiation conditions. At the second stage of this work the proton beams and the PTW 23343 Markus type ionization chamber used in clinical dosimetry were modeled. On the base of these models the perturbation factors 𝑝𝑤𝑎𝑙𝑙 , 𝑝𝑐𝑎𝑣 and the total perturbation factor 𝑝𝑞 were calculated for the punctual proton beam with energies of 15 MeV, 30 MeV, 60 MeV, 80 MeV. The influence of energy and spatial spread on the perturbation factors was determined. Moreover, the model of the real beam based on the passive system forming the proton beam in the Institute of Nuclear Physics of Polish Academy of Sciences in Bronowice was elaborated. The investigation basing on this model indicated that the depth-dose distributions obtained with the use of the water logic detectors and with the Marcus chamber model are compatible and they agree with the experimental results.
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Felcman, Adam. "Value at Risk: Historická simulace, variančně kovarianční metoda a Monte Carlo simulace." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-124888.

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The diploma thesis "Value at Risk: Historical simulation, variance covariance method and Monte Carlo" aims to value the risk which real bond portfolio bears. The thesis is decomposed into two major chapters: Theoretical and Practical chapters. The first one speaks about VaR and conditional VaR theory including their advantages and disadvantages. Moreover, there are described three basic methods to calculate VaR and CVaR with adjustments to each method in order to increase the reliability of results. The last chapter brings results of VaR and CVaR computation. Many graphs, tables and images are added to the result section in order to make the outputs more visible and well-arranged.
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Kučírek, Vojtěch. "Analýza spolehlivosti systémů metodou Monte Carlo." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-376990.

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Master’s thesis is focused on the technical systems reliability analysis. The first part of the thesis contains the description of the most commonly used reliability parameters and random variable probability distributions. Reliability of a human operator is described in the separate chapter. In the next part of the thesis are mentioned different types of reliability diagrams and methods of reliability analysis. Reliability analysis using Monte Carlo approach is described in the extra chapter. In the thesis are described several software tools, which can be used for systems reliability analysis. Design of PLC system with a human operator is done in the thesis. Reliability analysis using Monte Carlo approach is done on the designed PLC system. Results of Monte Carlo approach are compared with analytically calculated values and with values from reliability software.
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Novotný, Marek. "Programy pro výpočet nejistoty měření metodou Monte Carlo." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221220.

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The thesis deals with establishing uncertainties of indirect measurements. It focuses primarily on random number generators in software enabling the calculation of mea-surement uncertainties using Monte Carlo. Then it focuses on the uncertainty calculati-on indirect measurement as the Monte Carlo method and the classical numerical met-hod. The practical part deals with the verification of randomness generators numbers contained in various softwares. It also deals with the determination of uncertainties indi-rect current measurements by both above-mentioned methods and then comparing and evaluating the values achieved.
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Brlica, Pavel. "Stanovení nejistoty měření nano-CMM." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-377646.

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The topic of this master thesis is the issue of measurement uncertainty of nano-CMM, specifically SIOS NMM-1 machine. Theoretical part of the thesis consists of basic measurement uncertainty definitions, description of approaches to CMM measurement uncertainty and differences between classical CMM and nano-CMM. For measurement uncertainty calculation of nano-CMM, two method are chosen and adapted – substitution method and Monte Carlo method. These are applied in practical part for measurement uncertainty calculation of SIOS NMM-1 machine. Part of the practical part is performed measurement on machine in laboratory at the Czech Metrology institute in Brno. The outcome of this thesis is determination of measurement uncertainty of SIOS NMM-1.
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Gerold, Petr. "Zhodnocení investic s využitím metody Monte Carlo v programu Lumina Analytica." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-193755.

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This thesis focuses on investment, especially on the development of individual investments. It is concerned with stocks, bonds, mutual funds, saving accounts, real estates, commodities. The main objective is to create a model in Lumina Analytica. Interactive model should provides users (according to their filled values) the most likeliest appreciation of the selected portfolio investments. Supportive part of this thesis is an investment questionnaire. It obtains simplifield investment profile to potential investor and also a recommendation to which types of investments should investor invest. The purpose is to connect the subjective inestor view (the relationship of risk, return and liquidity) on investments with the real behavior of the individual investments with the help of analytical tools.
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Kouklík, Michal. "Zhodnocení investičního záměru dostavby JETE." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199563.

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The thesis deals with the project of the construction of the Temelin Nuclear Power Plant, as an optimal new production source of electrical energy to ensure the majority of the coverage of the growing electric consumption in the Czech Republic and to ensure the state energy independence in the future. The main part of the thesis is dedicated to the methods of strategic investments evaluation. Emphasis is placed on the dynamic methods, which are also working with the factors of time and risk, which are relevant in this case, because the project time horizon is 70 years. The investment project is evaluated from the perspective of owners, as well as from the overall perspective of owners and creditors. The Monte-Carlo method was implemented into the model to support the decision-making process and to move closer to reality. The method assigns the relevant distribution division to the model input values. The output is the set of available values, and the probability of their occurrence. The main thesis objective lies in the decision of the decision maker with a neutral attitude to risk, whether accept the investment or not.
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Gunia, Michał. "Radiative corrections and accuracy tests of Monte Carlo generators used at meson factories." Doctoral thesis, Katowice : Uniwersytet Śląski, 2013. http://hdl.handle.net/20.500.12128/5461.

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The amazing progress of research in the field of elementary particles in the second half of the twentieth century led to the formulation of the theory known as the Standard Model (SM). With this theory it was possible to connect in one model three of the four known fundamental interactions [1], At the same time, technological developments had allowed experimental methods to be improved. The accelerators used in experiments reached ever higher energies of accelerated particles while increasing the precision of the detectors. On the other hand, significant progress was made in the field of theoretical research and calculations. The predictions of the SM were confirmed by experiments, but the SM cannot be treated as a full theory of particle physics. First of all, the SM treats particle masses as parameters - they cannot be obtained by theoretical predictions. Furthermore, the SM does not describe particle physics phenomena such as dark matter or matter antimatter asymmetry, etc. This means that the SM should be considered as an effective theory. This is one of the reasons that suggest the need for searching for so called new physics or physics beyond the SM. Research conducted at high energies in the large hadron collider (LHC) could answer some of this question. However, there is a possibility of a parallel search for evidences of new physics. It is possible to test known parameters of the SM like for example the anomalous magnetic moment. This kind of test requires the comparison between the experimental measurement and the theoretical prediction done with extremely high precision. Thus, this advance of theoretical calculation and experimental methods has resulted in today’s tests of the SM requiring an inclusion of higher order effects. In an era of high energy measurements at the TeV level in the LHC, calculations and experiments conducted for low energy particle physics could play a crucial role in searching for traces of physics beyond the SM [2]. The hadronie contribution to the anomalous magnetic moment of the muon a^ld is an example of a quantity that depends strongly on low energy data. The low energy hadronie contributions are not calculable in the quantum chromodynamics (QCD) perturbation theory and the calculations require use of phenomenological models and the precise experimental data studies. The hadronie contribution to the anomalous magnetic moment of the muon a^ad is divided into three parts: the leading-order (LO) a^ad,LO and higher-order (HO) ah“d'HO vacuum polarisation contributions, and the lightby- light scattering contribution The leading order contribution can be obtained from the data for the processes e+e~ —> hadrons and using the dispersion integral it can be presented in the following form [3]: l o = / dsK{s)rTlutd{s) ( 1 .! ) ^ J where a had{s) is the total hadronie cross section (without the vacuum polarisation corrections). The K (s) function is called kernel function and is calculated within quantum electrodynamics (QED). The behaviour of this function shows that it decreases monotonically with increasing value of s. Therefore the a^ld'LO integrand is dominated by the hadron production below a few GeV. The hadron part of the anomalous magnetic moment of the muon obtained from e+e~ data is [4]: a had = (6 9 5 5 ± 4 9 ) . 1 0 - i i ( 1 2 ) Where: ahad,LO = (6 9 4 9 x ± 4 2 . 7 ) . i q - 11 (1 .3 ) a had,HO = ( _ 9 8 4 ± 0 .7 ) . l o - 11 (1 .4 ) ahadW = (1Q5 ± 26) . 10-11 ( j 5) The error for the leading order contribution is the biggest one. The sum of all contributions to the value of the anomalous magnetic moment of the muon obtained for the SM prediction is equal to: a lM = 116591828(50) • 1CT11 (1.6) The errors were added in quadrature. This value also contains ah“d. The value taken from the experiment gives: a™p = 116592089(63) • 10“n (1.7) A careful comparison of these two values gives the difference equal to 3.3 standard deviation: = a^p - a lM = 261(80) • l(Tn (1.8) This discrepancy could suggest the existence of some unknown effects, so it is very important to improve the accuracy of the experimental and theoretical value of to check if it is true. The uncertainty of theoretical calculations depends strongly on the hadronie contribution at low energies. The error of a^M is equal to 50 • 10~n , while the error of a^d,LO is equal to 42.7 • 10-11. So the biggest contribution to the error of aSM comes from the prediction of the leading-order hadronie contribution ,L°. The biggest contribution to the value of a^ad'LO comes from the region between 0.32 and 1.43 GeV and is equal to (6065±34)- 10~u [4]. The second in order is the region between 2 and 11.09 GeV where the contribution is equal to (411.9 ± 8.2) • 10-11. Here, for the region between 2.6 and 3.73 GeV for some channels, perturbative QCD (pQCD) was used. The contribution for energies above 11.09 GeV (obtained with pQCD) is equal to 0.211 • 10-11 and gives the error below 10-14. So the error of a^ad'LO is dominated by the low energy hadron production. This example shows the importance of results obtained for low energies where the use of perturbation methods for hadrons is not possible. A similar influence of low-energy data occurs for the hadronie part of the fine structure constant Aa had(Mz). The dispersion relation gives the following form of this magnitude [5]: £ } a»> Here the R(s) function depends on the total cross section ahad of the process e+e~ —> hadrons(muons) + 7 : = The error of A a had(Mz), at the level of about one percent, comes mainly from the process at the scale of about a few GeV [4], The precise determination of the value of Aa had(Mz) is, for example, necessary for the better determination of the Higgs mass . To increase accuracy of the theoretical value of ah“d or Aa had(Mz), it is necessary to improve the determination of the hadronie cross section. It is connected with the accuracy of the Monte Carlo (MC) generators used for the analysis of the experimental data. Generators like BabaYaga@NLO, MCGPJ and PHOKHARA are examples of generators that include NLO.
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Štolc, Zdeněk. "Metody výpočtu VaR pro tržní a kreditní rizika." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-4682.

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This thesis is focused on a theoretical explication of the basic methods of the calculation Value at Risk for the market and credit risk. For the market risk there is in detail developed the variance -- covariance method, historical simulation and Monte Carlo simulation, above all for the nonlinear portfolio. For all methods the assumptions of their applications are highlighted and the comparation of these methods is made too. For the credit risk there is made a theoretical description of CreditMetrics, CreditRisk+ and KMV models. Analytical part is concerned in the quantification of Value at Risk on two portfolios, namely nonlinear currency portfolio, which particular assumptions of the variance -- covariance method a Monte Carlo simulation are tested on. Then by these methods the calculation of Value at Risk is realized. The calculation of Credit Value at Risk is made on the portfolio of the US corporate bonds by the help of CreditMetrics model.
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Szewczuk, Marek. "Optymalizacja jakości obrazowania w mammografii cyfrowej z uwzględnieniem odpowiedzi detektora selenowego metodą symulaji Monte Carlo." Doctoral thesis, Katowice : Uniwersytet Śląski, 2013. http://hdl.handle.net/20.500.12128/5383.

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Rak piersi, jako najczęstszy nowotwór złośliwy wśród kobiet, jest poważnym problemem zdrowotnym i społecznym. Wcześnie wykryty może być jednak skutecznie leczony, co prowadzi do istotnej redukcji śmiertelności. Mammografia jest badaniem radiologicznym umożliwiającym wykrycie nowotworu we wczesnej fazie zaawansowania klinicznego i stosowana jest jako podstawowa metoda diagnostyczna w ramach programu przesiewowego. Z tego powodu aparaty mammograficzne muszą spełniać wysokie wymagania dotyczące jakości generowanych obrazów. Z drugiej strony, w związku z wykorzystaniem promieniowania jonizującego, istnieje konieczność minimalizacji dawki otrzymywanej przez pacjentki. Optymalizacja obu tych parametrów powinna uwzględniać m.in. charakterystykę odpowiedzi detektora obrazu oraz, zależny od gruczołowości piersi, fizyczny kontrast badanych struktur. Optymalizację tę przeprowadzono stosując techniki symulacji Monte Carlo.
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Books on the topic "Metoda Monte Carlo"

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Metod Monte-Karlo. 4th ed. Moskva: "Nauka," Glav. red. fiziko-matematicheskoĭ lit-ry, 1985.

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Lemieux, Christiane. Monte carlo and quasi-monte carlo sampling. New York: Springer, 2009.

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A, Whitlock Paula, and Wiley online library, eds. Monte Carlo methods. 2nd ed. Weinheim: Wiley-Blackwell, 2008.

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Kalos, Malvin H. Monte Carlo methods. New York: J. Wiley & Sons, 1986.

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Monte Carlo simulation. Thousand Oaks, Calif: Sage Publications, 1997.

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A, Whitlock Paula, ed. Monte Carlo methods. New York: J. Wiley & Sons, 1986.

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Monte Carlo: Concepts, algorithms, and applications. New York: Springer, 1996.

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Dunn, William L. Exploring Monte Carlo methods. Amsterdam: Elsevier/Academic Press, 2012.

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A primer for the Monte Carlo method. Boca Raton: CRC Press, 1994.

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Pierre, L' Ecuyer, and Owen Art B, eds. Monte Carlo and quasi-Monte Carlo methods 2008. Heidelberg: Springer, 2009.

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Book chapters on the topic "Metoda Monte Carlo"

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Brémaud, Pierre. "Monte Carlo." In Discrete Probability Models and Methods, 457–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43476-6_19.

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Barbu, Adrian, and Song-Chun Zhu. "Cluster Sampling Methods." In Monte Carlo Methods, 123–88. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_6.

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Liou, William W. "Monte Carlo Method." In Encyclopedia of Microfluidics and Nanofluidics, 2315–19. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-5491-5_1059.

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Crépey, Stéphane. "Monte Carlo Methods." In Springer Finance, 161–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37113-4_6.

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Albert, Jim, and Maria Rizzo. "Monte Carlo Methods." In R by Example, 307–36. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1365-3_13.

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Knechtli, Francesco, Michael Günther, and Michael Peardon. "Monte Carlo Methods." In SpringerBriefs in Physics, 35–53. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-024-0999-4_2.

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Koch, Karl-Rudolf. "Monte Carlo Methods." In Mathematische Geodäsie/Mathematical Geodesy, 445–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-55854-6_100.

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Cragg, John G. "Monte Carlo Methods." In The New Palgrave Dictionary of Economics, 9128–30. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_733.

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Joyce, Philip. "Monte Carlo Methods." In Numerical C, 147–56. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5064-8_7.

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Ross, J., and A. Marshak. "Monte Carlo Methods." In Photon-Vegetation Interactions, 441–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-75389-3_14.

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Conference papers on the topic "Metoda Monte Carlo"

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Chong, Jike, Ekaterina Gonina, and Kurt Keutzer. "Monte Carlo methods." In the 2010 Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1953611.1953626.

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KALOS, M. H. "MONTE CARLO METHODS." In Edward Teller Centennial Symposium - Modern Physics and the Scientific Legacy of Edward Teller. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789812838001_0008.

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Sendov, Blagovest H., and Ivan Dimov. "MONTE CARLO METHODS AND PARALLEL ALGORITHMS." In International Youth Workshop on Monte Carlo Methods and Parallel Algorithms. WORLD SCIENTIFIC, 1991. http://dx.doi.org/10.1142/9789814540193.

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Luttman, Aaron, and Matthias Morzfeld. "What the collapse of the ensemble Kalman filter tells us about localization of particle filters." In 12th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing in Stanford, CA August 14-19, 2016. US DOE, 2016. http://dx.doi.org/10.2172/1755194.

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Basden, Alastair, Richard Myers, and Timothy Butterley. "Monte-Carlo simulation of EAGLE." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/aopt.2009.aotud4.

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FAURE, HENRI. "MONTE-CARLO AND QUASI-MONTE-CARLO METHODS FOR NUMERICAL INTEGRATION." In Present and Future. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799890_0001.

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Frenkel, D. "Biased Monte Carlo Methods." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632121.

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Wilding, Nigel B. "Phase Switch Monte Carlo." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632147.

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Harrison, Robert L., Carlos Granja, and Claude Leroy. "Introduction to Monte Carlo Simulation." In NUCLEAR PHYSICS METHODS AND ACCELERATORS IN BIOLOGY AND MEDICINE: Fifth International Summer School on Nuclear Physics Methods and Accelerators in Biology and Medicine. AIP, 2010. http://dx.doi.org/10.1063/1.3295638.

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Zimmerman, George B. "Monte Carlo methods in ICF." In LASER INTERACTION AND RELATED PLASMA PHENOMENA. ASCE, 1997. http://dx.doi.org/10.1063/1.53528.

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Reports on the topic "Metoda Monte Carlo"

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Vogel, Thomas. Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148317.

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Hungerford, Aimee L. (U) Introduction to Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1351179.

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Hill, James Lloyd. Introduction to the Monte Carlo Method. Office of Scientific and Technical Information (OSTI), June 2020. http://dx.doi.org/10.2172/1634920.

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Jerome Spanier. Third International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC98). Office of Scientific and Technical Information (OSTI), March 1999. http://dx.doi.org/10.2172/761782.

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Brown, Forrest B. Advanced Computational Methods for Monte Carlo Calculations. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1417155.

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Caflisch, Russel E. Rarefied Gas Dynamics and Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada295375.

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Creutz, M. Lattice gauge theory and Monte Carlo methods. Office of Scientific and Technical Information (OSTI), November 1988. http://dx.doi.org/10.2172/6530895.

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Ayoul-Guilmard, Q., F. Nobile, S. Ganesh, M. Nuñez, R. Tosi, C. Soriano, and R. Rosi. D5.5 Report on the application of multi-level Monte Carlo to wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.03.

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Abstract:
We study the use of multi-level Monte Carlo methods for wind engineering. This report brings together methodological research on uncertainty quantification and work on target applications of the ExaQUte project in wind and civil engineering. First, a multi-level Monte Carlo for the estimation of the conditional value at risk and an adaptive algorithm are presented. Their reliability and performance are shown on the time-average of a non-linear oscillator and on the lift coefficient of an airfoil, with both preset and adaptively refined meshes. Then, we propose an adaptive multi-fidelity Monte Carlo algorithm for turbulent fluid flows where multilevel Monte Carlo methods were found to be inefficient. Its efficiency is studied and demonstrated on the benchmark problem of quantifying the uncertainty on the drag force of a tall building under random turbulent wind conditions. All numerical experiments showcase the open-source software stack of the ExaQUte project for large-scale computing in a distributed environment.
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Blomquist, R. N., and E. M. Gelbard. Alternative implementations of the Monte Carlo power method. Office of Scientific and Technical Information (OSTI), March 2002. http://dx.doi.org/10.2172/793906.

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Owen, Richard Kent. Quantum Monte Carlo methods and lithium cluster properties. Office of Scientific and Technical Information (OSTI), December 1990. http://dx.doi.org/10.2172/10180548.

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