Excerpt from A Non-Random Sampling Method
During the past ten years, several studies have been made of systematic (non-random) sampling methods, for calculations of the monte Carlo type, based on theorems of H. Weyl[l] on the asymptotic distribution, in n-space, of a set of points generated by congruences. The object is to find a computing method, similar to monte Carlo, in which the error of the approximation decreases more rapidly, with increasing sampie size, than when random sampling is used. Although the method has not yet achieved success in practical applications, there seems to be enough interest in it to warrant giving an account.
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Paperback. Condizione: New. Print on Demand. This fascinating book proposes a method to enhance Monte Carlo simulations by introducing a new non-random sampling approach grounded in mathematical theories. By replacing the random generation of points in multi-dimensional space with carefully constructed sequences derived from algebraic principles, this method has the potential to yield more accurate results for complex simulations, particularly those with high dimensionality. The author provides theoretical foundations for the method, supported by statistical analysis and numerical examples. The book opens up new avenues for researchers and practitioners seeking to improve the efficiency and precision of their Monte Carlo simulations, a technique widely used in fields such as physics, finance, and engineering. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Codice articolo 9781332421251_0
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