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Exit Condition for Probabilistic Assessment Using Monte Carlo Method

References KONEČNÝ, P. Vliv počtu náhodně proměnných na přesnost odhadu pravděpodobnosti poruchy metodou Monte Carlo (The Influence of the Number of Random Variables on the Probability of Failure Estimate in Monte Carlo Method). Sborník vědeckých prací VŠB-TUO, řada stavební , 2009, s. 173-180. ISSN 1213-1962. (In Czech) MAREK, P., GUŠTAR, M., ANANOS, T. Simulation-Based Reliability Assesment for Structural Engineers. Boca Raton: CRC Press, 1995. ISBN 0-8493-8286-6. PRAKS, P

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-87. [4] Kołowrocki K. and Soszyńska-Budny J.: Reliability and Safety of Complex Systems and Processes: Modeling - Identification - Prediction - Optimization. Springer, 2011. [5] Kuligowska E.: Preliminary Monte Carlo approach to complex system reliability analysis. Summer Safety and Reliability Seminars - SSARS 2012, Journal of Polish Safety and Reliability Association, Vol. 3, No1,2012,s.59-71. [6] Soszyńska J.: Systems reliability analysis in variable operation conditions. International Journal of Reliability, Quality and Safety

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Assesing multileaf collimator effect on the build-up region using Monte Carlo method

References Heath E, Seuntjens J. Development and validation of a BEAMnrc component module for accurate Monte Carlo modelling of the Varian dynamic Millenium multileaf collimator. Phys Med Biol. 2003; 48 : 4045-4063. Jang SY, Vassiliev ON, Liu HH, Mohan R, Siebers JV. Development and commissioning of a multileaf collimator model in Monte Carlo dose calculations for intensity-modulated radiation therapy. Med Phys. 2006; 33 (3): 770-781. Kim JO, Siebers JV, Keall PJ, Arnfield MR

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Monte Carlo modelling of Th-Pb fuel assembly with californium neutron source

., & Kumar, A. (1984). Blanket design and calculated performance for the Lotus Fusion-Fission Hybrid Experimental Devices Test Facility. Fusion Sci. Technol ., 2, 189–208. 5. Bhabha Atomic Research Centre. [access: 10.11.2017], . 6. Oettingen, M., Cetnar, J., & Mirowski, T. (2015). The MCB code for numerical modeling of fourth generation nuclear reactors. Computer Sci. , 16 (4), 329–350. 7. X-5 Monte Carlo Team. (2005). MCNP – A General Monte Carlo N-Particle Transport Code , Version 5 . LANL. (Report LA-UR-03

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On the Monte Carlo Matrix Computations on Intel MIC Architecture

References 1. Alexandrov, V. N., O. A. Esquivel-Flores. Towards Monte Carlo Preconditioning Approach and Hybrid Monte Carlo Algorithms for Matrix Computations. - Computers & Mathematics with Applications, Vol. 70, 2015, Issue 11, pp. 2709-2718. ISSN 0898-1221. 2. Alexandrov, V., A. Karaivan o v a. Parallel Monte Carlo Algorithms for Sparse SLAE Using MPI. - In: J. Dongarra, E. Luque, T. Margalef, Eds. LNCS. Vol. 1697. Springer, 1999, pp. 283-290. 3. Alexandrov, V., O

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Forecasting Currency Risk in an Enterprise Using the Monte Carlo Simulation

Bibliography Bagnato L., Potì V., Zoia M., 2015, The role of orthogonal polynomials in adjusting hyperbolic secant and logistic distributions to analyse financial asset returns , Statistical Papers 56(4), pp. 1205-1234. Best P., 2000, Wartość narażona na ryzyko , Oficyna Ekonomiczna, Kraków. Brigo D., Dalessandro A., Neugebauer M., Triki F., 2007, A Stochastic Processes Toolkit for Risk Management , (31.07.2017). Glasserman P., 2004, Monte Carlo Methods in Financial Engineering , Springer, New York

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Practical Procedure for Position Tolerance Uncertainty Determination via Monte-Carlo Error Propagation

References De Bièvre, P., (1997). Measurement results without statements of reliability (uncertainty) should not be taken seriously. Accreditation and Quality Assurance 2 (6), p. 269. ISO GUM (1995). Guide to the expression of uncertainty in measurement , BIPM, IEC, IFCC, ISO, IUPAC and OIML, ISBN 92-67-10188-9, Second Edition. Cox, M. G., Harris, P. M., Siebert B. R.-L. (2003). Evaluation of measurement uncertainty based on the propagation of distributions using Monte Carlo

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Monte Carlo calculated CT numbers for improved heavy ion treatment planning

References 1. Mustafa, A. A., & Jackson, D. F. (1983). The relation between X-ray CT numbers and charged particle stopping powers and its significance for radiotherapy treatment planning. Phys. Med. Biol., 28, 169-176. 2. Verhaegen, F., & Devic, S. (2005). Sensitivity study for CT images in Monte Carlo treatment planning. Phys. Med.Biol., 50, 937-946. 3. Homolka, P., Gahleitner, A., & Nowotny, R. (2002).Temperature dependence of HU values for various water equivalent phantom materials. Phys. Med. Biol., 47, 2917

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An Efficient Monte Carlo Approach to Compute PageRank for Large Graphs on a Single PC

References [1] K. Avrachenkov, N. Litvak, D. Nemirovsky, and N. Osipova. Monte carlo methods in pagerank computation: When one iteration is sufficient. SIAM Jounal on Numerical Analysis , (2):890–904, February 2007. [2] Bahman Bahmani, Kaushik Chakrabarti, and Dong Xin. Fast personalized pagerank on mapreduce. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data , pages 973–984, 2011. [3] Bahman Bahmani, Abdur Chowdhury, and Ashish Goel. Fast incremental and personalized pagerank. VLDB Endow. , 4

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Electricity Price Forecasting Using Monte Carlo Simulation: The Case of Lithuania

References Amelin M. (2004). On Monte Carlo Simulation and Analysis of Electricity Markets. Doctoral Thesis. Royal Institute of Technology. Benth, F.E., Cartea, Á. and Kiesel, R. (2008). Pricing forward contracts in power markets by the certainty equivalence principle: Explaining the sign of the market risk premium. Journal of Banking & Finance, 32(10), 2006-2021. Benth, F.E., Kiesel, R. and Nazarova, A. (2011). A critical empirical study of three electricity spot price models. Energy Economics

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