Monte-Carlo simulations are applied to estimate the characteristic ratios and p parameters Iwhich is defined as the ratio of the radius of gyration to the hydrodynamic radius) from the RIS models for PE, POM, polybutadiena, and polyisoprene. [Pg.75]

Monte Carlo simulation An iterative resampling technique frequently used in uncertainty analysis in risk assessments to estimate the distribution of a model s output parameter. [Pg.275]

Monte Carlo simulation (Monte Carlo technique) A repeated random sampling from the distribution of values for each of the parameters in a generic (exposure or dose) equation to derive an estimate of the distribution of (exposures or doses in) the population (USEPA, 1992a,b, 1997c IRIS, 1999 AlHA, 2000). [Pg.399]

Monte Carlo simulation can be used to explore the influence of random noise on the parameter estimation. Consider the following protocol [Pg.405]

The program is capable of performing Monte Carlo simulation on several input parameters (i.e. infiltration rate, emission rate, decay rate and outdoor concentration) for developing a range of estimates for zone-specific concentrations or inhalation exposure. [Pg.231]

We have presented applications of a parameter estimation technique based on Monte Carlo simulation to problems in polymer science involving sequence distribution data. In comparison to approaches involving analytic functions, Monte Carlo simulation often leads to a simpler solution of a model particularly when the process being modelled involves a prominent stochastic coit onent. [Pg.293]

The uncertainty of the fitted values of these two parameters has been estimated objectively by means of a Monte-Carlo simulation model. The data points on each curve in Figure 5 are the mean of 100 calculated points and each point is the "best-fit" of the parameter to a simulated measurement in a simulated indoor environment in which allowance is made for fluctuations of the parameters. [Pg.313]

The third method used to interpret the level of risk associated with chlorpy-rifos use is Monte Carlo simulation. This method provides a range of exposure estimates for the evaluation of the uncertainty in a risk estimate based on ranges of input variables. The first step in performing a Monte Carlo simulation is determination of a model to describe the dose. This model describes the relationship between the input parameters and dose, and a specific model is presented here for one group of workers. [Pg.38]

Calculations of the incident electron penetration depth into the dielectric layer is a well understood phenomenon [58,59] in recent years many Monte Carlo simulation tools were developed to study it. In our case it allowed an easy calculation of the photoresist layer thickness for different exposure parameters of the eb. For example if the eb exposure is done with Vo = 15 kV accelerating voltage and the dielectric layer is selected to be polymethylmethacrylate (pmma), we estimate the penetration depth by Kanaya and Okayama s [58] expression [Pg.198]

In classical statistics, the most important type of criterion for judging estimators is a high probability that a parameter estimate will be close to the actual value of the parameter estimated. To implement the classical approach, it is necessary to quantify the closeness of an estimate to a parameter. One may rely on indices of absolute, relative, or squared error. Mean squared error (MSB) has often been used by statisticians, perhaps usually because of mathematical convenience. However, if estimators are evaluated using Monte Carlo simulation it is easy to use whatever criterion seems most reasonable in a given situation. [Pg.37]

Eurthermore, uncertainties in the exposure assessment should also be taken into account. However, no generally, internationally accepted principles for addressing these uncertainties have been developed. For predicted exposure estimates, an uncertainty analysis involving the determination of the uncertainty in the model output value, based on the collective uncertainty of the model input parameters, can be performed. The usual approach for assessing this uncertainty is the Monte Carlo simulation. This method starts with an analysis of the probability distribution of each of the variables in the uncertainty analysis. In the simulation, one random value from each distribution curve is drawn to produce an output value. This process is repeated many times to produce a complete distribution curve for the output parameter. [Pg.349]

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