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Monte Carlo Methods and Applications

Managing Editor: Sabelfeld, Karl K.

Editorial Board: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis

4 Issues per year


CiteScore 2017: 0.67

SCImago Journal Rank (SJR) 2017: 0.417
Source Normalized Impact per Paper (SNIP) 2017: 0.860

Mathematical Citation Quotient (MCQ) 2017: 0.25

Online
ISSN
1569-3961
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Volume 20, Issue 4

Issues

Uncertainty quantification of world population growth: A self-similar PDF model

Stefan Heinz
Published Online: 2014-10-31 | DOI: https://doi.org/10.1515/mcma-2014-0005

Abstract

The uncertainty of world population growth represents a serious global problem. Existing methods for quantifying this uncertainty face a variety of questions. An essential problem of these methods is the lack of direct evidence for their validity, for example by means of comparisons with independent observations like measurements. A way to support the validity of such forecast methods is to validate these models with reference models, which play the role of independent observations. Desired properties of such a reference model are formulated here. A new reference world population model is formulated by a probabilistic extension of recent deterministic UN projections. This model is validated in terms of theory and observations: it is shown that the model has all desired properties of a reference model, and its predictions are very well supported by the known world population development from 1980 till 2010. Applications of this model as a reference model demonstrate the advantages of the stochastic world population model presented here.

Keywords: World population growth; UN world population forecasts; uncertainty quantification; stochastic world population model

MSC: 60H25; 60H30

About the article

Received: 2014-05-03

Accepted: 2014-10-20

Published Online: 2014-10-31

Published in Print: 2014-12-01


Citation Information: Monte Carlo Methods and Applications, Volume 20, Issue 4, Pages 261–277, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/mcma-2014-0005.

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