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master's thesis
Monte Carlo metoda aproksimacije
2024. urn:nbn:hr:126:503055
Hankić, Benjamin
Josip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics

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Hankić, B. (2024). Monte Carlo metoda aproksimacije (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics. Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:503055

Hankić, Benjamin. "Monte Carlo metoda aproksimacije." Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. https://urn.nsk.hr/urn:nbn:hr:126:503055

Hankić, Benjamin. "Monte Carlo metoda aproksimacije." Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. https://urn.nsk.hr/urn:nbn:hr:126:503055

Hankić, B. (2024). 'Monte Carlo metoda aproksimacije', Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, accessed 02 February 2025, https://urn.nsk.hr/urn:nbn:hr:126:503055

Hankić B. Monte Carlo metoda aproksimacije [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics; 2024 [cited 2025 February 02] Available at: https://urn.nsk.hr/urn:nbn:hr:126:503055

B. Hankić, "Monte Carlo metoda aproksimacije", Master's thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:126:503055

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Metadata
TitleMonte Carlo metoda aproksimacije
Title (english)Monte Carlo Approximation Method
AuthorBenjamin Hankić
MentorIvan Papić (mentor)
Committee memberNenad Šuvak (predsjednik povjerenstva)
Committee memberIvan Papić (član povjerenstva)
Committee memberDanijel Grahovac (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics
Osijek
Defense date and country2024-10-28, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Probability Theory and Statistics
Abstract
Tema ovog rada je Monte Carlo metoda koja koristi slučajne brojeve za aproksimaciju, što je posebno korisno za složene ili visokodimenzionalne funkcije. Ključna prednost ove metode je njena sposobnost da se nosi s problemima visoke dimenzionalnosti, gdje druge metode postaju neučinkovite. Glavni nedostatak Monte Carlo metode je njena spora konvergencija. Točnost aproksimacije se sporo povećava s povećanjem broja simulacija. U ovom kontekstu, generatori slučajnih brojeva su ključni za stvaranje nasumičnih uzoraka koji se koriste u simulacijama.Kvaliteta rezultata Monte Carlo metode uvelike ovisi o kvaliteti generatora slučajnih brojeva, jer loše generirani uzorci mogu dovesti do netočnih aproksimacija. Dakle, pri izboru i testiranju generatora slučajnih brojeva moramo biti vrlo pažljivi jer su oni od velike važnosti za uspješnost primjene Monte Carlo metode aproksimacije.
Abstract (english)
The topic of this thesis is the Monte Carlo method, which uses random numbers to estimate the value of integrals, particularly for complex or high-dimensional functions. The key advantage of this method is its ability to handle high-dimensional problems where other methods become inefficient. However, the main drawback of the Monte Carlo method is its slow convergence, meaning that the accuracy of the estimate increases slowly with the number of simulations. In this context, random number generators play a crucial role in generating the random samples used in the simulations. The quality of the Monte Carlo method’s results largely depends on the quality of the random number generators, as poorly generated samples can lead to inaccurate estimates. Therefore, careful selection and testing of random number generators are essential for the successful application of the Monte Carlo method.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:503055
Study programmeTitle: Mathematics; specializations in: Financial and Statistical Mathematics, Mathematics and Computer Science, Industrial and Applied Mathematics
Course: Financial and Statistical Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-10-29 11:33:26