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Jan F. Van Impe - One of the best experts on this subject based on the ideXlab platform.

  • Monte Carlo Analysis as a tool to incorporate variation on experimental data in predictive microbiology
    Food Microbiology, 2003
    Co-Authors: Filip Poschet, Nico Scheerlinck, A.h. Geeraerd, Bart Nicolai, Jan F. Van Impe
    Abstract:

    Until now, most of the mathematical models used in predictive microbiology are deterministic, i.e. their outcome is a point estimate for the microbial load at a certain time instant. For more advanced exploitation of predictive microbiology in the context of hazard Analysis and critical control points and risk Analysis studies, stochastic models should be developed. Such models predict a probability mass function for the microbial load at a certain time instant. The objective of this paper is to illustrate methodologically how to generate, starting from the experimental observations and a deterministic growth model, probability density functions for (i) the model parameters and (ii) the predictions as a function of time, by using Monte Carlo Analysis. A normal distribution over the experimental data was considered. This probabilistic approach, incorporating experimental variation, is applied to experimental growth data of Escherichia coli K12 and Listeria innocua ATCC 33090.

  • Monte Carlo Analysis as a tool to incorporate variation on experimental data in predictive microbiology
    Food Microbiology, 2003
    Co-Authors: Filip Poschet, Nico Scheerlinck, A.h. Geeraerd, Bart Nicolai, Jan F. Van Impe
    Abstract:

    Until now, most of the mathematical models used in predictive microbiology are deterministic, i.e. their outcome is a point estimate for the microbial load at a certain time instant. For more advanced exploitation of predictive microbiology in the context of hazard Analysis and critical control points and risk Analysis studies, stochastic models should be developed. Such models predict a probability mass function for the microbial load at a certain time instant. The objective of this paper is to illustrate methodologically how to generate, starting from the experimental observations and a deterministic growth model, probability density functions for (i) the model parameters and (ii) the predictions as a function of time, by using Monte Carlo Analysis. A normal distribution over the experimental data was considered. This probabilistic approach, incorporating experimental variation, is applied to experimental growth data of Escherichia coli K12 and Listeria innocua ATCC 33090.

Jon C. Helton - One of the best experts on this subject based on the ideXlab platform.

  • latin hypercube sampling and the propagation of uncertainty in analyses of complex systems
    Reliability Engineering & System Safety, 2003
    Co-Authors: Jon C. Helton, Freddie J. Davis
    Abstract:

    Abstract The following techniques for uncertainty and sensitivity Analysis are briefly summarized: Monte Carlo Analysis, differential Analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol' variance decomposition, and fast probability integration. Desirable features of Monte Carlo Analysis in conjunction with Latin hypercube sampling are described in discussions of the following topics: (i) properties of random, stratified and Latin hypercube sampling, (ii) comparisons of random and Latin hypercube sampling, (iii) operations involving Latin hypercube sampling (i.e. correlation control, reweighting of samples to incorporate changed distributions, replicated sampling to test reproducibility of results), (iv) uncertainty Analysis (i.e. cumulative distribution functions, complementary cumulative distribution functions, box plots), (v) sensitivity Analysis (i.e. scatterplots, regression Analysis, correlation Analysis, rank transformations, searches for nonrandom patterns), and (vi) analyses involving stochastic (i.e. aleatory) and subjective (i.e. epistemic) uncertainty.

  • Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems
    2002
    Co-Authors: Jon C. Helton, Freddie J. Davis
    Abstract:

    The following techniques for uncertainty and sensitivity Analysis are briefly summarized: Monte Carlo Analysis, differential Analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol’ variance decomposition, and fast probability integration. Desirable features of Monte Carlo Analysis in conjunction with Latin hypercube sampling are described in discussions of the following topics: (i) properties of random, stratified and Latin hypercube sampling, (ii) comparisons of random and Latin hypercube sampling, (iii) operations involving Latin hypercube sampling (i.e. correlation control, reweighting of samples to incorporate changed distributions, replicated sampling to test reproducibility of results), (iv) uncertainty Analysis (i.e. cumulative distribution functions, complementary cumulative distribution functions, box plots), (v) sensitivity Analysis (i.e. scatterplots, regression Analysis, correlation Analysis, rank transformations, searches for nonrandom patterns), and (vi) analyses involving stochastic (i.e. aleatory) and subjective (i.e. epistemic) uncertainty. Published by Elsevier Science Ltd.

  • uncertainty and sensitivity Analysis techniques for use in performance assessment for radioactive waste disposal
    Reliability Engineering & System Safety, 1993
    Co-Authors: Jon C. Helton
    Abstract:

    Abstract Uncertainty and sensitivity Analysis techniques for use in performance assessments for radioactive waste disposal are reviewed. Summaries are given for the following techniques: differential Analysis, Monte Carlo Analysis, response surface methodology, and Fourier amplitude sensitivity test. Of these techniques, Monte Carlo Analysis is felt to be the most widely applicable for use in performance assessment. Monte Carlo Analysis involves five steps: (1) selection of a range and distribution for each input variable; (2) generation of a sample from the input variables; (3) propagation of the sample through the model under consideration; (4) performance of uncertainty Analysis; and (5) performance of sensitivity Analysis. These steps are discussed and illustrated with an Analysis performed as part of a preliminary performance assessment for the Waste Isolation Pilot Plant (WIPP).

Tom Dhaene - One of the best experts on this subject based on the ideXlab platform.

  • stochastic macromodeling of nonlinear systems via polynomial chaos expansion and transfer function trajectories
    IEEE Transactions on Microwave Theory and Techniques, 2014
    Co-Authors: Domenico Spina, Luc Knockaert, Dimitri De Jonghe, Dirk Deschrijver, Georges Gielen, Tom Dhaene
    Abstract:

    A novel approach is presented to perform stochastic variability Analysis of nonlinear systems. The versatility of the method makes it suitable for the Analysis of complex nonlinear electronic systems. The proposed technique is a variation-aware extension of the Transfer Function Trajectory method by means of the Polynomial Chaos expansion. The accuracy with respect to the classical Monte Carlo Analysis is verified by means of a relevant numerical example showing a simulation speedup of 1777×.

  • variability Analysis of multiport systems via polynomial chaos expansion
    IEEE Transactions on Microwave Theory and Techniques, 2012
    Co-Authors: Domenico Spina, Luc Knockaert, Tom Dhaene, Francesco Ferranti, G Antonini, Dries Vande Ginste
    Abstract:

    We present a novel technique to perform variability Analysis of multiport systems. The versatility of the proposed technique makes it suitable for the Analysis of different types of modern electrical systems (e.g., interconnections, filters, connectors). The proposed method, based on the calculation of a set of univariate macromodels and on the use of the polynomial chaos expansion, produces a macromodel of the transfer function of the multiport system including its statistical properties. The accuracy and the significant speed up with respect to the classical Monte Carlo Analysis are verified by means of two numerical examples.

Domenico Spina - One of the best experts on this subject based on the ideXlab platform.

  • stochastic macromodeling of nonlinear systems via polynomial chaos expansion and transfer function trajectories
    IEEE Transactions on Microwave Theory and Techniques, 2014
    Co-Authors: Domenico Spina, Luc Knockaert, Dimitri De Jonghe, Dirk Deschrijver, Georges Gielen, Tom Dhaene
    Abstract:

    A novel approach is presented to perform stochastic variability Analysis of nonlinear systems. The versatility of the method makes it suitable for the Analysis of complex nonlinear electronic systems. The proposed technique is a variation-aware extension of the Transfer Function Trajectory method by means of the Polynomial Chaos expansion. The accuracy with respect to the classical Monte Carlo Analysis is verified by means of a relevant numerical example showing a simulation speedup of 1777×.

  • variability Analysis of multiport systems via polynomial chaos expansion
    IEEE Transactions on Microwave Theory and Techniques, 2012
    Co-Authors: Domenico Spina, Luc Knockaert, Tom Dhaene, Francesco Ferranti, G Antonini, Dries Vande Ginste
    Abstract:

    We present a novel technique to perform variability Analysis of multiport systems. The versatility of the proposed technique makes it suitable for the Analysis of different types of modern electrical systems (e.g., interconnections, filters, connectors). The proposed method, based on the calculation of a set of univariate macromodels and on the use of the polynomial chaos expansion, produces a macromodel of the transfer function of the multiport system including its statistical properties. The accuracy and the significant speed up with respect to the classical Monte Carlo Analysis are verified by means of two numerical examples.

Filip Poschet - One of the best experts on this subject based on the ideXlab platform.

  • Monte Carlo Analysis as a tool to incorporate variation on experimental data in predictive microbiology
    Food Microbiology, 2003
    Co-Authors: Filip Poschet, Nico Scheerlinck, A.h. Geeraerd, Bart Nicolai, Jan F. Van Impe
    Abstract:

    Until now, most of the mathematical models used in predictive microbiology are deterministic, i.e. their outcome is a point estimate for the microbial load at a certain time instant. For more advanced exploitation of predictive microbiology in the context of hazard Analysis and critical control points and risk Analysis studies, stochastic models should be developed. Such models predict a probability mass function for the microbial load at a certain time instant. The objective of this paper is to illustrate methodologically how to generate, starting from the experimental observations and a deterministic growth model, probability density functions for (i) the model parameters and (ii) the predictions as a function of time, by using Monte Carlo Analysis. A normal distribution over the experimental data was considered. This probabilistic approach, incorporating experimental variation, is applied to experimental growth data of Escherichia coli K12 and Listeria innocua ATCC 33090.

  • Monte Carlo Analysis as a tool to incorporate variation on experimental data in predictive microbiology
    Food Microbiology, 2003
    Co-Authors: Filip Poschet, Nico Scheerlinck, A.h. Geeraerd, Bart Nicolai, Jan F. Van Impe
    Abstract:

    Until now, most of the mathematical models used in predictive microbiology are deterministic, i.e. their outcome is a point estimate for the microbial load at a certain time instant. For more advanced exploitation of predictive microbiology in the context of hazard Analysis and critical control points and risk Analysis studies, stochastic models should be developed. Such models predict a probability mass function for the microbial load at a certain time instant. The objective of this paper is to illustrate methodologically how to generate, starting from the experimental observations and a deterministic growth model, probability density functions for (i) the model parameters and (ii) the predictions as a function of time, by using Monte Carlo Analysis. A normal distribution over the experimental data was considered. This probabilistic approach, incorporating experimental variation, is applied to experimental growth data of Escherichia coli K12 and Listeria innocua ATCC 33090.