English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The origin of the “Monte Carlo method“ historically comes from Los Alamos National Laboratory in the early years after the Second World War and is related with two important events that took place in 1945: the successful test at Alamogordo and the building of the first electronic computer (the ENIAC) (Metropolis, 1987). After the review of the ENIAC results held in the spring of 1946 at Los Alamos, Stanislaw Ulam realized that statistical techniques in combination with the surprising speed and the versatility of this electronic computer could lead to a powerful tool in theoretical calculations (Metropolis, 1987). On March 11, 1947, John von Neumann in a handwritten letter included a detailed outline of a possible statistical approach (incorporating Stanislaw s idea) to solving the problem of neutron diffusion in a spherical core of fissionable material surrounded by a shell of tamper material. At that time, Nick Metropolis suggested the name “Monte Carlo” for the re-emerging sampling technique (Metropolis, 1987). However, a previous work performed by Enrico Fermic in neutron diffusion in Rome in the early thirties, incorporated the essential principles of the Monte Carlo method when he was studying the moderation of neutrons (Metropolis, 1987). Another example of the previous use of this sampling technique is an ancient problem in geometric probability: “The Buffon’s needle problem” which was stated in 1733 and solved (solution published in 1777) by Geroges Louis Leclerc, Comte de Buffon (1707-1788) (Weisstein, accessed September 2010). In this problem the probability of the needle crossing a line (in a table marked with equidistant parallel lines) is proportional to the inverse of the number π provided that the length of the needle is less than the space between lines. The repetition of this experiment many times results into the assessment of π. During the post-war period the Monte Carlo (MC) method spread into many fields of knowledge such as astrophysics, solid state, optics, etc. One example in astrophysics is the study of the transfer of visible radiation through terrestrial clouds using a MC computer program that incorporated the Henyey-Greenstein phase function to describe the scattering process of water droplet clouds (Danielson et al., 1969). Another illustration of this method appears in the book “The Monte Carlo Methods in Atmospheric Optics” (Marchuk et al., 1980) which had a first Russian edition in 1974. In the preface of this book an acceptable definition of the MC method is given which is reproduced here:

Monte Carlo Modeling of Light Propagation in Neonatal Skin