The Monte Carlo method in science and engineering

Amar, Jacques G.

doi:10.1109/mcse.2006.34

Cited by 97 publications

(71 citation statements)

References 61 publications

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“…A Monte Carlo simulation is a method evaluating a deterministic model using sets of random numbers (or rather, pseudorandom numbers) as inputs, often iteratively [18][19]. Sets of random numbers complying with a chosen probability distribution function are generated, these numbers are included in more or less complex computations, and the outcome is evaluated mathematically.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Can the prevalence of high blood drug concentrations in a population be estimated by analysing oral fluid? A study of tetrahydrocannabinol and amphetamine

Gjerde

Verstraete

2010

Forensic Science International

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A B S T R A C TAim: To study several methods for estimating the prevalence of high blood concentrations of tetrahydrocannabinol and amphetamine in a population of drug users by analysing oral fluid (saliva).Methods: Five methods were compared, including simple calculation procedures dividing the drug concentrations in oral fluid by average or median oral fluid/blood (OF/B) drug concentration ratios or regression coefficients and more complex Monte Carlo simulations.Populations of 311 cannabis users and 197 amphetamine users from the Rosita-2 Project were studied. Results:The results of a feasibility study suggested that the Monte Carlo simulations might give better accuracies than simple calculations if good data on OF/B ratios is available. If using only 20 randomly selected OF/B ratios, a Monte Carlo simulation gave the best accuracy but not the best precision. Dividing by the OF/B regression coefficient gave acceptable accuracy and precision, and was therefore the best method. None of the methods gave acceptable accuracy if the prevalence of high blood drug concentrations was less than 15%. Conclusion:Dividing the drug concentration in oral fluid by the OF/B regression coefficient gave an acceptable estimation of high blood drug concentrations in a population, and may therefore give valuable additional information on possible drug impairment, e.g. in roadside surveys of drugs and driving. If good data on the distribution of OF/B ratios are available, a Monte Carlo simulation may give better accuracy.

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Section: Monte Carlo Simulationsmentioning

confidence: 99%

Can the prevalence of high blood drug concentrations in a population be estimated by analysing oral fluid? A study of tetrahydrocannabinol and amphetamine

Gjerde

Verstraete

2010

Forensic Science International

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“…Since the original article published by Metropolis and Ulam (1949) on application of the Monte Carlo Method in mathematical physics, researchers have applied the Monte Carlo method to a wide range of nonequilibrium and equilibrium processes and to a variety of complex problems (Amar, 2006). Kawk and Ingall (2007) explored the applications of Monte Carlo simulation for managing project risks and uncertainties.…”

Section: Case Study With Monte Carlo Simulationmentioning

confidence: 99%

A Probabilistic Capital Budgeting Model for New Product Development under Stage-Gate Process

Sharifzadeh

Sharif²

2015

AFR

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Stage-gate model for new product development (NPD) and for any research and development (R&D) project consists of a series of stages and gates; each stage is a set of research activities and gates are milestones at which decisions are made based on predetermined criteria. In this paper we developed a capital budgeting model for a typical 5-stage, 5-gate process to be used for appraising commercial viability of the NPD at Stage 2 which will serve as one of the decision making criterion at Gate 3. Because in an NPD project, completion costs (capital investments), completion times, and future cash flows are uncertain, we developed the Pert-Beta probability distribution to encounter for the randomness of these variables. Furthermore, we suggested the model can be applied to real case NPD projects with the Monte Carlo Simulation approach. We then verified the workability of the model by applying it to a notional NPD project and demonstrated that all decision variables reach steady state in the Monte Carlo Simulation calculations.

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“…We can see from (29) and (30) thatR yT contains 3D parameters, namely elevation/azimuth angles and range. Since the constructed matrixR yT , which is from a NLA with 2M + 1 sensors along Y axis, can be considered as the covariance matrix generated by a ULA with 4M − 1 sensors along Y axis, thus the eigendecomposition of the constructed matrixR yT can be written…”

Section: Azimuth Angle Estimationmentioning

confidence: 99%

“…The input signal-to-noise ratio (SNR) of the k-th source is defined as 10 log 10 (σ 2 k /σ 2 n ). The parameter estimation performance of the proposed algorithm and the algorithm in [20] is compared in terms of the root mean square error (RMSE) [25,29], and the RMSE of the k-th signal source is defined by RMSE = (…”

Section: Simulationsmentioning

confidence: 99%

“…Monte Carlo method is algorithm for solving various kinds of computational problems by using random numbers. Because of the repetitive nature of a typical Monte Carlo algorithm, as well as the large number of calculations involved, the Monte Carlo method is particularly suited to calculation using a computer [29,30]. In the following each experiment, the elevation/azimuth angle and range RMSE of the proposed algorithm and the algorithm in [20] is derived from 500 independent trials.…”

Section: Trials And∂ (Q)mentioning

confidence: 99%

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Localization of 3d Near-Field Source Using the Aperture Extension Method and Nonuniform Cross Array

Jiang¹,

Duan²,

Li³

et al. 2013

PIER B

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Abstract-Depending on the aperture extension (AE), a high performance three-dimensional (3D) near-field (NF) source localization algorithm is proposed with the nonuniform linear array (NLA). The proposed algorithm first generates some fictitious sensors to extend the array aperture by constructing a new Toeplitz matrix, and then obtains a two-dimensional (2D) covariance matrix which only contains the elevation angle and range parameters, and another 3D covariance matrix which contains the elevation/azimuth angle and range parameters. Then based on the 2D covariance matrix, both the elevation angle and range parameters are estimated by using the NLA along the Z axis. With the estimates of both the elevation angle and range parameters and combining the 3D covariance matrix, the estimates of the azimuth angle parameters are obtained using the NLA along the Y axis. The proposed algorithm has four main merits: i) unlike some classical NF source localization algorithms, the quarter-wavelength sensor spacing constraint is not required and more sources can be located simultaneously by the proposed algorithm; ii) the 3D parameters of the proposed algorithm are paired automatically; iii) the 3D search required in conventional 3D multiple signal classification (MUSIC) algorithm is replaced with only one-dimensional (1D) search, and thus the computational burden is reduced; iv) the proposed algorithm gains superior parameter estimation accuracy and resolution.

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The Monte Carlo method in science and engineering

Cited by 97 publications

References 61 publications

Can the prevalence of high blood drug concentrations in a population be estimated by analysing oral fluid? A study of tetrahydrocannabinol and amphetamine

Can the prevalence of high blood drug concentrations in a population be estimated by analysing oral fluid? A study of tetrahydrocannabinol and amphetamine

A Probabilistic Capital Budgeting Model for New Product Development under Stage-Gate Process

Localization of 3d Near-Field Source Using the Aperture Extension Method and Nonuniform Cross Array

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