Metoda Monte Carlo

Metode Monte Carlo so stohastične (deterministične) simulacijske metode ali algoritmi, ki s pomočjo naključnih ali kvazinaključnih števil in velikega števila izračunov in ponavljanja omogočajo predvidevanje obnašanja zapletenih matematičnih sistemov.

Zgodovina

Prvotno so bile iznajdene v državnem laboratoriju v mestu Los Alamos v ZDA nedolgo po koncu 2. svetovne vojne. Takrat je bilo v ZDA ravno končan prvi elektronski računski stroj in znanstveniki v Los Alamosu so razmišljali o tem, kako bi se ga dalo najbolje izkoristiti za razvoj jedrskega orožja (vodikove bombe). Leta 1946 je Ulam predlagal uporabo naključnega vzorčenja za simulacijo potovanja nevtronov in von Neumann je predlog leta 1947 realiziral. S tem so bile omogočene simulacije preprostih razmer, ki pa so bile vseeno pomembne za uspešno izvedbo projekta. Ulam in Metropolis sta leta 1949 objavila članek, v katerem sta opisala svoje ideje, čemur je sledilo mnogo raziskav tekom 1950-ih let. Metode so dobile ime po glavnem mestu države Monako, ki je znano po svojih igralnicah in igrah na srečo (ime je predlagal Metropolis, eden od pionirjev te metode).

Uporaba

V ekonomiji se uporabljajo za računanje poslovnega tveganja, spremembo vrednosti investicij, pri strateškem planiranju ipd.

V medicinski fiziki in radioterapiji se uporablja za načrtovanje doze za obsevanje tumorjev.

Viri

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Binder, Kurt (1995). The Monte Carlo Method in Condensed Matter Physics. New York: Springer. ISBN 0-387-54369-4.
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Zunanje povezave

Wikimedijina zbirka ponuja več predstavnostnega gradiva o temi: metoda Monte Carlo.

Weisstein, Eric Wolfgang. »Monte Carlo Method«. MathWorld.
Feynman-Kac models and particle Monte Carlo algorithms Arhivirano 2012-05-01 na Wayback Machine.
Introduction to Monte Carlo Methods, Computational Science Education Project
The Basics of Monte Carlo Simulations Arhivirano 2012-08-30 na Wayback Machine., University of Nebraska-Lincoln
Introduction to Monte Carlo simulation (for Microsoft Excel), Wayne L. Winston
Monte Carlo Simulation for MATLAB and Simulink
Monte Carlo Methods – Overview and Concept Arhivirano 2015-08-10 na Wayback Machine., brighton-webs.co.uk
Monte Carlo techniques applied in physics Arhivirano 2016-03-04 na Wayback Machine.
Approximate And Double Check Probability Problems Using Monte Carlo method Arhivirano 2012-03-10 na Wayback Machine. at Orcik Dot Net
Monte Carlo simulation using mathematica at Wolfram Mathematica
Eric Grimson; John Guttag. »Lecture 20: Monte Carlo Simulations, Estimating pi«. Introduction to Computer Science and Programming stimating pi. MIT Open Courseware. Pridobljeno 4. februarja 2015.