journal: Monte Carlo Methods and Applications

Impact Factor: 0.8

Published since January 1, 1995

Monte Carlo Methods and Applications

ISSN: 1569-3961

Managing Editor: Karl K. Sabelfeld

Editorial Board: Kurt Binder, Nicolas Bouleau, Alexandre J. Chorin, Ivan Dimov, Alain Dubus, Alexander D. Egorov, Sergei M. Ermakov, John H. Halton, Stefan Heinrich, Chi-Ok Hwang, Malvin H. Kalos, Dmitry R. Kolyukhin, D. Lepingle, Yaohang Li, Roman Makarov, Michael Mascagni, Peter Mathe, Harald Niederreiter, Giray Ökten , Shigeyoshi Ogawa, Eckhard Platen, Brian R. Sawford, Wolfgang Ch. Schmid, John Schoenmakers, Irina A. Shalimova, Ilya M. Sobol, Jerry Spanier, Denis Talay, Wolfgang Wagner

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About this journal

Objective
Monte Carlo Methods and Applications is a quarterly published journal that presents original articles on the theory and applications of Monte Carlo and Quasi-Monte Carlo methods. Launched in 1995 the journal covers all stochastic numerics topics with emphasis on the theory of Monte Carlo methods and new applications in all branches of science and technology. Stochastic models in all fields of applied sciences, in particular turbulence, rarefied gas dynamics and nanotechnology, bioscience, medicine, chemical kinetics and combustion, stochastic models in mathematical finance

Topics

Theory of Monte Carlo methods
Quasi-Monte Carlo methods
Integration
Boundary value problems for PDE's
Numerics of stochastic differential equations
Simulation of random variables, stochastic processes and fields

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Research articles

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In-depth discussions crossing the boundaries of many disciplines
State-of-the-art research on theory and applications of Monte Carlo Methods
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Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 30 (2024)

Volume 29 (2023)

Volume 28 (2022)

Volume 27 (2021)

Volume 26 (2020)

Volume 25 (2019)

Volume 24 (2018)

Volume 23 (2017)

Volume 22 (2016)

Volume 21 (2015)

Volume 20 (2014)

Volume 19 (2013)

Volume 18 (2012)

Volume 17 (2011)

Volume 16 (2010)

Volume 15 (2009)

Volume 14 (2008)

Volume 13 (2008)

Volume 12 (2006)

Volume 11 (2005)

Volume 10 (2004)

Volume 9 (2003)

Volume 8 (2002)

Volume 7 (2001)

Volume 6 (2000)

Volume 5 (1999)

Volume 4 (1998)

Volume 3 (1997)

Volume 2 (1996)

Volume 1 (1995)

Journal Impact Factor	0.8	2023, Journal Citation Reports (Clarivate, 2024)
5-year Journal Impact Factor	0.8	2023, Journal Citation Reports (Clarivate, 2024)
Journal Citation Indicator	0.33	2023, Journal Citation Reports (Clarivate, 2024)
CiteScore	1.2	2023, Scopus (Elsevier B.V., 2024)
SCImago Journal Rank	0.253	2023, SJR (Scimago Lab, 2024; Data Source: Scopus)
Source Normalized Impact per Paper	0.521	2023, CWTS Journal Indicators (CWTS B.V., 2024; Data Source: Scopus)
Mathematical Citation Quotient	0.10

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Editorial

Managing Editor
Karl K. Sabelfeld 
Institute of Computational Mathematics and Mathematical Geophysics
Academy of Sciences
Siberian Branch
Lavrentjeva 6
630090 Novosibirsk, Russia
E-mail: karl@osmf.sscc.ru, sabelfeld.karl@yahoo.de
 
Editorial Board
Kurt Binder 
Institut für Physik
Johannes Gutenberg Universität Mainz
Staudinger Weg 7
55099 Mainz, Germany
E-mail: binder@chaplin.physik.uni-mainz.de

Nicolas Bouleau 
Ecoles des Ponts
28 rue des Saints Peres
75007 Paris, France
E-mail: nb@enpc.fr

Alexandre J. Chorin 
Department of Mathematics
University of California
Berkeley, CA 94720-3840, USA
E-mail: chorin@math.berkeley.edu

Ivan Dimov 
Bulgarian Academy of Sciences
Acad. G. Bonchev St. 25A
1113 Sofia, Bulgaria
E-mail: ivdimov@bas.org

Alain Dubus 
Service de Metrologie Nucleaire CP165/84
Université Libre de Bruxelles
Av. F.D. Roosevelt 50
1050 Brussels, Belgium
E-mail: adubus@ulb.ac.be

Alexander D. Egorov 
Belarus Academy of Sciences
Surganova St. 11
Minsk 220072, Belarus
E-mail: egorov@im.bas-net.by

Sergei M. Ermakov 
Department of Mathematics and Mechanics
St. Petersburg State University
Bibliotechnaya Sq. 2
Petrodvorets
198904 St. Petersburg, Russia
E-mail: sergej.ermakov@pobox.spbu.ru

John H. Halton 
University of North Carolina
305 Sitterson Hall
C.B. 3175 Chapel Hill
NC 27599-3175, USA
E-mail: halton@cs.unc.edu

Stefan Heinrich 
Universität Kaiserslautern
Postfach 3049
67653 Kaiserslautern, Germany
E-mail: heinrich@informatik.uni-kl.de

Chi-Ok Hwang
School of Liberal Arts and Sciences
Gwangju Institute of Science and Technology
Gwangju, 61005 Republic of Korea
E-mail: chwang@gist.ac.kr

Malvin H. Kalos 
Lawrence Livermore National Laboratory
Livermore, CA 94551, USA

Dmitry R., Kolyukhin
Trofimuk Institute of Petroleum Geology and Geophysics SB RAS,
3 Koptug ave.
630090 Novosibirsk, Russia
E-mail: KolyukhinDR@ipgg.sbras.ru

Dominique Lepingle 
Department of Mathematics
University of Orléans
B.P. 6759
45067 Orléans cedex 2, France
E-mail: lepingle@labomath.univ-orleans.fr

Yaohang Li
Department of Computer Science
Old Dominion University
4700 Elkhorn Ave, Suite 3300
Norfolk, VA 23529-0162
USA
yaohang@cs.odu.edu

Roman Makarov 
Department of Mathematics
Wilfrid Laurier University
75 University Avenue West
Waterloo, Ontario N2L 3C5, Canada
E-mail: rmakarov@wlu.ca

Michael Mascagni 
Department of Computer Science
Florida State University
203 Love Building
Tallahassee, FL 32306-4530, USA
E-mail: mascagni@cs.fsu.edu

Peter Mathé 
Weierstraß-Institut für Angewandte Analysis und Stochastik
Mohrenstr. 39
10117 Berlin, Germany
E-mail: mathe@wias-berlin.de

Harald Niederreiter 
Austrian Academy of Sciences
Dr. Ignaz Seipel-Platz 2
1010 Wien
E-mail: Harald.Niederreiter@oeaw.ac.at

Shigeyoshi Ogawa 
Department of Mathematics
Ritsumeikan University
Kyoto, Japan
E-mail: ogawa-s@se.ritsumei.ac.jp

Giray Ökten 
Department of Mathematics
Florida State University
Tallahassee, FL 32306-4510, USA
E-mail: okten@math.fsu.edu

Eckhard Platen 
School of Finance and Economics
University of Technology, Sydney
PO Box 123
Broadway NSW 2007, Australia
E-mail: eckhard.platen@uts.edu.au

Brian R. Sawford 
Department of Mechanical Engineering
Monash University
Building 33
Clayton Campus
Wellington Road
Clayton, VIC 3800, Australia
E-mail: brian.sawford@.eng.monash.au

Wolfgang C. Schmid 
Department of Mathematics
University of Salzburg
Hellbrunnerstr. 34
5020 Salzburg, Austria
E-mail: wolfgang.schmid@sbg.ac.at

John Schoenmakers 
Weierstraß-Institut für Angewandte Analysis und Stochastik
Mohrenstr. 39
10117 Berlin, Germany.
E-mail: schoenma@wias-berlin.de

Irina A. Shalimova
Institute of Computational Mathematics and Mathematical Geophysics,
Russian Acad.Sci,
6 Lavrentiev str.,
630090, Novosibirsk, Russia
E-mail: ias@osmf.sscc.ru

Ilya M. Sobol 
Institute for Mathematical Modelling
Russian Academy of Sciences
Miusskaya Square 4
125047 Moscow, Russia
E-mail: sobol@ciam.ru

Jerry Spanier 
Beckman Laser Institute & Medical Cliic
1002 Health Sciences Road East
Irvine, CA 92612, USA
E-mail: jerome.spanier@cgu.edu

Denis Talay 
INRIA
2004 Route des Lucioles
B.P. 109
06561 Valbonne cedex, France
E-mail: talay@sophia.inria.fr

Wolfgang Wagner 
Weierstraß-Institut für Angewandte Analysis und Stochastik
Mohrenstr. 39
10117 Berlin, Germany
E-mail: wagner@wias-berlin.de

Volume 30 Issue 4

December 2024

Publicly Available December 1, 2024

Frontmatter

Page range: i-iv

Download PDF

Open Access August 8, 2024

Investigating the ecological fallacy through sampling distributions constructed from finite populations

David J. Torres, Damain Rouson

Page range: 331-363

More Download PDF

Abstract

Correlation coefficients and linear regression values computed from group averages can differ from correlation coefficients and linear regression values computed using individual scores. This observation known as the ecological fallacy often assumes that all the individual scores are available from a population. In many situations, one must use a sample from the larger population. In such cases, the computed correlation coefficient and linear regression values will depend on the sample that is chosen and the underlying sampling distribution. The sampling distribution of correlation coefficients and linear regression values for group averages will be identical to the sampling distribution for individuals for normally distributed variables for random samples drawn from infinitely large continuous distributions. However, data that is acquired in practice is often acquired when sampling without replacement from a finite population. Our objective is to demonstrate through Monte Carlo simulations that the sampling distributions for correlation and linear regression will also be similar for individuals and group averages when sampling without replacement from normally distributed variables. These simulations suggest that when a random sample from a population is selected, the correlation coefficients and linear regression values computed from individual scores will not be more accurate in estimating the entire population values compared to samples when group averages are used as long as the sample size is the same.

Open Access September 21, 2024

Bernoulli factory: The 2𝚙-coin problem

Shenggang Hu, Bo Zhang, Hongsheng Dai, Wei Liang

Page range: 365-374

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Abstract

This paper aims to address the Bernoulli factory problem of 2 ⁢ p 2\mathtt{p} coins by analysing the relationship between the negative binomial distributions and binomial distributions generated on the same chain of coin flips. The proposed algorithm requires fewer conditions on the constructed sequences compared with the existing algorithms. The feasibility of obtaining such 2 ⁢ p 2\mathtt{p} -coin based on 𝚙-coins will be considered as well.

November 14, 2024

Randomized vector algorithm with iterative refinement for solving boundary integral equations

Karl K. Sabelfeld, Georgy Agarkov

Page range: 375-388

Abstract

This study is a follow-up of two our papers (Appl. Math. Lett. 126 (2022) and MCMA 29:4 (2023)), where we developed a vector randomized algorithms with iterative refinement for large system of linear algebraic equations. We focus in this paper on the application of the vector randomized iterative refinement algorithm to boundary integral equations that solve interior Dirichlet and exterior Neumann boundary value problems for 2D Laplace equation.

October 2, 2024

Application of semiclassical approximation to stochastic differential equations

Victor Malyutin, Berdakh Nurjanov

Page range: 389-395

Abstract

A method for calculating the characteristics of stochastic differential equations using semiclassical approximation is proposed. For a stochastic differential equation arising in the study of the pure birth process (Yule process) and the Cox Ingersoll Ross (CIR) model, an analysis of the accuracy of the semiclassical approximation was carried out. This analysis is based on a comparison of approximate values with exact values for the mathematical expectation of a solution to the equation.

October 16, 2024

Trapezoidal and Simpson’s methods with a random design

Thibaud Kaeshammer, Christian Paroissin, Herna Urmeneta

Page range: 397-411

Abstract

The aim of the present paper is first to propose a state-of-art of this domain. Second, some convergence results are established in the case of the Dirichlet distribution. This distribution has the advantage to include both the uniform case and the deterministic one. In a first part, the Dirichlet distribution is defined and some properties are supplied. In a second part, new results are established in connexion with former theorems.

September 26, 2024

Periodic INAR(1) model with Bell innovations distribution

Abderrahmen Manaa

Page range: 413-430

Abstract

In this paper, we introduce a class of periodic integer-valued autoregressive BL-PINAR(1) models with Bell innovations distribution based on the binomial thinning operator. The basic probabilistic and statistical properties of this class are studied. Indeed, the first and the second moment periodically stationary conditions are established. The closed forms of these moments are, under the obtained conditions, derived. Furthermore, the periodic autocovariance structure is also considered while providing the closed form of the periodic autocorrelation function. The conditional least squares (CLS), Yule–Walker (YW), weighted conditional least squares (WCLS), and conditional maximum likelihood (CML) methods are applied to estimate the underlying parameters. The asymptotic properties of the CLS and the YW estimators are obtained. The performances of these methods are compared through a simulation study. An application on a real data set is provided.

November 14, 2024

Effect of biaxial strain on the binding energies of adsorbed In and Al atoms on (001) surfaces of InAs and AlAs

Ivan A. Aleksandrov, Dmitry V. Gulyaev, Eugeny A. Kolosovsky, Konstantin S. Zhuravlev

Page range: 431-436

Abstract

The dependence of the binding energies of adsorbed In and Al atoms on the (001) surfaces of InAs and AlAs with β 2 {\beta_{2}} (2 × {\times} 4) reconstruction on the biaxial compression and tension strains has been investigated using the density functional theory. The maps of the potential energy surface of the adsorbed In atom on the (001) surface of InAs and AlAs with β 2 {\beta_{2}} (2 × {\times} 4) reconstruction have been calculated. According to the calculation results, a general trend towards an increase in the absolute values of the binding energy of the adsorbed In and Al atoms on the (001) surface of InAs and AlAs with an increase in the biaxial tension strain has been observed. For certain positions of the adsorbed atoms, a change in the sign of the derivative of the binding energy dependence on the biaxial strain has been observed with a change in the symmetry of the adsorbed state of the atom.

October 2, 2024

Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T ₁-weighted MRI using a three level hierarchical Bayesian model

Kahina Bouchebbah, Nabil Zougab

Page range: 437-448

Abstract

Nowadays, Dynamic Contrast Enhanced MRI (DCE-MRI) is becoming the most widely explored technique in clinical practice for tumor assessment. In acquiring DCE-MRI, a contrast agent (CA), also called tracer, is injected into the blood flow before or during the acquisition of a time series of T 1 {T_{1}} -weighted images with fast imaging techniques. When the CA goes through the tissue, MR signal intensity measurements in voxels of the region of interest (ROI) are registered and used to calculate the CA concentration in each voxel. The Tofts models have become standard for the analysis of DCE-MRI and which express tissue CA concentration C ⁢ ( t ) {C(t)} as function of time t . The analysis of quantitative parameters in DCE-MRI provides the quantitative criterion as a reference rather than relying only on the shape of the DCE-curve, as it is used for diagnosis of prostate cancer (PCa). This study aim to provide a new thinking in quantitative analysis which may therefore improve diagnostic accuracy for detection of prostate cancer and could be used in patient baseline prediction and guide management. A hierarchical Bayesian model was built to estimate the values of the four pharmacokinetic parameters ( K trans {K_{\mathrm{trans}}} , k ep {k_{\mathrm{ep}}} , υ p {\upsilon_{\mathrm{p}}} , υ e {\upsilon_{\mathrm{e}}} ) for both prostate healthy and lesion tissues in the peripheral zone. This estimation is important because it help to understand the behavior of the CA in the body and how this latter reacts to the CA in order to emphasize the expectation or the absence of prostate lesion during the diagnosis step.

November 14, 2024

Bayesian analysis of the COVID-19 pandemic using a Poisson process with change-points

Masoud Majidizadeh

Page range: 449-465

Abstract

Analyzing COVID-19 data presents a challenge in Bayesian computations of the Poisson process because the experimental conditions are not under control. This lack of homogeneity can lead to inconsistent model parameters, which violates the assumptions of Bayesian inference. In this paper, we study the multiple change-point detection problem from this viewpoint for a non-homogeneous sample path of the Poisson process as the response variable. The rate parameters are linked to some explanatory using a generalized linear model. The number of change-points is considered to be unknown as well as their locations. We introduce a Bayesian paradigm to estimate the number and location of change-points. We also present an adaptive RJMCMC algorithm to generate pseudo-random samples from the posterior distributions. We apply the proposed model to analyze the COVID-19 infection curves from different countries and identify patterns of cases. We also assess the efficacy of interventions, such as vaccination and public health emergency responses, implemented by different countries. The results of the analysis provide valuable insights into the spread of COVID-19 and the effectiveness of interventions. The proposed model can be used to inform public health decision-making and help to improve the management of the pandemic.

(Deutsch)

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Online ISSN: 1569-3961
Print ISSN: 0929-9629
Type: Journal
Language: English
Publisher: De Gruyter
First published: January 1, 1995
Publication Frequency: 4 issues per year
Audience: researchers in the field of theory and applications of Monte Carlo and Quasi-Monte Carlo methods

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Monte Carlo Methods and Applications

Frontmatter

Investigating the ecological fallacy through sampling distributions constructed from finite populations

Abstract

Bernoulli factory: The 2𝚙-coin problem

Abstract

Randomized vector algorithm with iterative refinement for solving boundary integral equations

Abstract

Application of semiclassical approximation to stochastic differential equations

Abstract

Trapezoidal and Simpson’s methods with a random design

Abstract

Periodic INAR(1) model with Bell innovations distribution

Abstract

Effect of biaxial strain on the binding energies of adsorbed In and Al atoms on (001) surfaces of InAs and AlAs

Abstract

Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T 1-weighted MRI using a three level hierarchical Bayesian model

Abstract

Bayesian analysis of the COVID-19 pandemic using a Poisson process with change-points

Abstract

Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T ₁-weighted MRI using a three level hierarchical Bayesian model