The Experts below are selected from a list of 574521 Experts worldwide ranked by ideXlab platform
Overstall, Antony M. - One of the best experts on this subject based on the ideXlab platform.
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Properties of using Fisher information gain for Bayesian Design of Experiments
2021Co-Authors: Overstall, Antony M.Abstract:The Bayesian decision-theoretic approach to Design of Experiments involves specifying a Design (values of all controllable variables) to maximise the expected utility function (expectation with respect to the distribution of responses and parameters). For most common utility functions, the expected utility is rarely available in closed form and requires a computationally expensive approximation which then needs to be maximised over the space of all possible Designs. This hinders practical use of the Bayesian approach to find experimental Designs. However, recently, a new utility called Fisher information gain has been proposed. The resulting expected Fisher information gain reduces to the prior expectation of the trace of the Fisher information matrix. Since the Fisher information is often available in closed form, this significantly simplifies approximation and subsequent identification of optimal Designs. In this paper, it is shown that for exponential family models, maximising the expected Fisher information gain is equivalent to maximising an alternative objective function over a reduced-dimension space, simplifying even further the identification of optimal Designs. However, if this function does not have enough global maxima, then Designs that maximise the expected Fisher information gain lead to non-identifiablility
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Properties of using Fisher information gain for Bayesian Design of Experiments
2021Co-Authors: Overstall, Antony M.Abstract:The Bayesian decision-theoretic approach to Design of Experiments involves specifying a Design (values of all controllable variables) to maximise the expected utility function (expectation with respect to the distribution of responses and parameters). For most common utility functions, the expected utility is rarely available in closed form and requires a computationally expensive approximation which then needs to be maximised over the space of all possible Designs. This hinders practical use of the Bayesian approach to find experimental Designs. However, recently, a new utility called Fisher information gain has been proposed. The resulting expected Fisher information gain reduces to the prior expectation of the trace of the Fisher information matrix. Since the Fisher information is often available in closed form, this significantly simplifies approximation and subsequent identification of optimal Designs. In this paper, it is shown that for exponential family models, maximising the expected Fisher information gain is equivalent to maximising an alternative function over a reduced-dimension space, simplifying even further the identification of optimal Designs. However, if this function does not have enough maxima, then all Designs that maximise the expected Fisher information gain are under-supported, i.e. have less support points than unknown parameters, leading to parameter redundancy
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Bayesian decision-theoretic Design of Experiments under an alternative model
2021Co-Authors: Overstall, Antony M., Mcgree, James M.Abstract:Traditionally Bayesian decision-theoretic Design of Experiments proceeds by choosing a Design to minimise expectation of a given loss function over the space of all Designs. The loss function encapsulates the aim of the experiment, and the expectation is taken with respect to the joint distribution of all unknown quantities implied by the statistical model that will be fitted to observed responses. In this paper, an extended framework is proposed whereby the expectation of the loss is taken with respect to a joint distribution implied by an alternative statistical model. Motivation for this includes promoting robustness, ensuring computational feasibility and for allowing realistic prior specification when deriving a Design. To aid in exploring the new framework, an asymptotic approximation to the expected loss under an alternative model is derived, and the properties of different loss functions are established. The framework is then demonstrated on a linear regression versus full-treatment model scenario, on estimating parameters of a non-linear model under model discrepancy and a cubic spline model under an unknown number of basis functions.Comment: Supplementary material appears as an appendi
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Bayesian decision-theoretic Design of Experiments under an alternative model
2020Co-Authors: Overstall, Antony M., Mcgree, James M.Abstract:Traditionally Bayesian decision-theoretic Design of Experiments proceeds by choosing a Design to minimize expectation of a loss function over the space of all Designs. The loss represents the aim of the experiment and expectation is taken with respect to the joint distribution of all unknown quantities implied by the statistical model that will be fitted to observed responses. An extended framework is proposed whereby expectation of the loss is taken with respect to a joint distribution implied by an alternative statistical model. The framework can be employed to promote robustness, to ensure computational feasibility or to allow realistic prior specification. An asymptotic approximation to the resulting expected loss is developed to aid in exploring the framework and, in particular, on the implications of the choice of loss function. The framework is demonstrated on a linear regression versus full-treatment model scenario, and on estimating parameters of a non-linear model under differing model discrepancies
Alexander Penlidis - One of the best experts on this subject based on the ideXlab platform.
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Reactivity Ratio Estimation in Radical Copolymerization: From Preliminary Estimates to Optimal Design of Experiments
Industrial & Engineering Chemistry Research, 2014Co-Authors: Niousha Kazemi, Thomas A. Duever, Benoît H. Lessard, Milan Marić, Alexander PenlidisAbstract:An error-in-variables-model (EVM) framework is presented for the optimal estimation of reactivity ratios in copolymerization systems. This framework consists of several sequential steps and practical prescriptions that can yield reliable and statistically correct reactivity ratio values. These steps include: (a) screening Experiments for estimating preliminary reactivity ratios, (b) optimal Design of Experiments, (c) full conversion range Experiments and estimation of optimal reactivity ratios, and if necessary, (d) Design of sequentially optimal Experiments and re-estimation of reactivity ratios and diagnostic checks. This complete methodology should become common practice for determining reactivity ratios with the highest possible level of confidence. The performance of this framework is verified experimentally with data from the controlled nitroxide-mediated copolymerization of 9-(4-vinylbenzyl)-9H-carbazole (VBK) and methyl methacrylate (MMA), a novel and largely unstudied copolymer system.
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Design of Experiments for reactivity ratio estimation in multicomponent polymerizations using the error in variables approach
Macromolecular Theory and Simulations, 2013Co-Authors: Niousha Kazemi, Thomas A. Duever, Alexander PenlidisAbstract:Model-based Design of Experiments using the error-in-variables model (EVM) is explored. The fundamental differences between DOE in the traditional nonlinear regression versus the EVM context are discussed, and it is pointed out that for cases where there are errors in all variables, using the EVM Design criterion is the only appropriate approach. In addition, the implementation of the EVM Design criterion and its characteristics for both initial and sequential Design schemes are discussed. The main application is the implementation of the EVM criterion to Design optimal trials for reliable estimating reactivity ratios for typical copolymerization systems, along with prescriptions for the practitioner.
Alexei A Lapkin - One of the best experts on this subject based on the ideXlab platform.
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self optimisation and model based Design of Experiments for developing a c h activation flow process
Beilstein Journal of Organic Chemistry, 2017Co-Authors: Alexander Walter Wilhelm Echtermeyer, Yehia Amar, Jacek Zakrzewski, Alexei A LapkinAbstract:A recently described C(sp3)–H activation reaction to synthesise aziridines was used as a model reaction to demonstrate the methodology of developing a process model using model-based Design of Experiments (MBDoE) and self-optimisation approaches in flow. The two approaches are compared in terms of experimental efficiency. The self-optimisation approach required the least number of Experiments to reach the specified objectives of cost and product yield, whereas the MBDoE approach enabled a rapid generation of a process model.
Anders Brundin - One of the best experts on this subject based on the ideXlab platform.
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bioprocess optimization using Design of Experiments methodology
Biotechnology Progress, 2008Co-Authors: Carl-fredrik Mandenius, Anders BrundinAbstract:This review surveys recent applications of Design-of-Experiments (DoE) methodology in the development of biotechnological processes. Methods such as factorial Design, response surface methodology, and (DoE) provide powerful and efficient ways to optimize cultivations and other unit operations and procedures using a reduced number of Experiments. The multitude of interdependent parameters involved within a unit operation or between units in a bioprocess sequence may be substantially refined and improved by the use of such methods. Other bioprocess-related applications include strain screening evaluation and cultivation media balancing. In view of the emerging regulatory demands on pharmaceutical manufacturing processes, exemplified by the process analytical technology (PAT) initiative of the United States Food and Drug Administration, the use of experimental Design approaches to improve process development for safer and more reproducible production is becoming increasingly important. Here, these options are highlighted and discussed with a few selected examples from antibiotic fermentation, expanded bed optimization, virus vector transfection of insect cell cultivation, feed profile adaptation, embryonic stem cell expansion protocols, and mammalian cell harvesting.
Felipe A C Viana - One of the best experts on this subject based on the ideXlab platform.
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a tutorial on latin hypercube Design of Experiments
Quality and Reliability Engineering International, 2016Co-Authors: Felipe A C VianaAbstract:The growing power of computers enabled techniques created for Design and analysis of simulations to be applied to a large spectrum of problems and to reach high level of acceptance among practitioners. Generally, when simulations are time consuming, a surrogate model replaces the computer code in further studies (e.g., optimization, sensitivity analysis, etc.). The first step for a successful surrogate modeling and statistical analysis is the planning of the input configuration that is used to exercise the simulation code. Among the strategies devised for computer Experiments, Latin hypercube Designs have become particularly popular. This paper provides a tutorial on Latin hypercube Design of Experiments, highlighting potential reasons of its widespread use. The discussion starts with the early developments in optimization of the point selection and goes all the way to the pitfalls of the indiscriminate use of Latin hypercube Designs. Final thoughts are given on opportunities for future research. Copyright © 2015 John Wiley & Sons, Ltd.
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an algorithm for fast optimal latin hypercube Design of Experiments
International Journal for Numerical Methods in Engineering, 2009Co-Authors: Felipe A C Viana, Gerhard Venter, Vladimir BalabanovAbstract:CITATION: Viana, F. A. C., Venter, G. & Balabanov, V. 2010. An algorithm for fast optimal Latin hypercube Design of Experiments. International Journal for Numerical Methods in Engineering, 82(2):135-156, doi:10.1002/nme.2750.
