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Published byLeilani Dennison Modified about 1 year ago

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Simon Fraser University Department of Statistics and Actuarial Sciences Some Random Questions

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Simon Fraser University Department of Statistics and Actuarial Sciences Questions I have…many not smart “Parameterization” – Came up several time –Can be choice for stochastic features in a computer model –Can be parameters in PDE’s…do these have error? How to account for? Robert and Howard – How did you generate your ensembles –Wanted to understand sensitivity to certain parameters? How measure?

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Simon Fraser University Department of Statistics and Actuarial Sciences Questions I have…many not smart NCAR folks.. What was helpful or what did you learn? Statisticians… new problems or new methodology?

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Simon Fraser University Department of Statistics and Actuarial Sciences Questions I have…many not smart Regarding PDE’s: y~N(pde( ), ) ? Build physics right in? Elaine…interested in maximums (Bo?)….failure models in Engineering

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Simon Fraser University Department of Statistics and Actuarial Sciences Questions I have…many not smart Guillaume – Added stochastic forcing…are models still closed Seem to have a lot of parameters…are they identifiable? I do not think I understand the data assimilation (Josh? Jeff?)

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Simon Fraser University Department of Statistics and Actuarial Sciences GP’s have proven effective for emulating computer model output & data mining Gaussian Spatial Process (GP) model frequently used in modeling response from complex computer codes Emulating computer model output – output varies smoothly with input changes – output is essentially noise free – GP’s outperform other modeling approaches in this arena (mars, cart, …) Data Mining – compares favorably with other machine learning techniques – noise is a more prominent feature

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Simon Fraser University Department of Statistics and Actuarial Sciences Gaussian Process Models Emulators to be used as a surrogate for the computer model 1.How to build likely model complexity into design/analysis –GP models are very complex and hard to interpret –Even more challenging in calibration/assimilation problems 2.Sample Size Issues –Do you have enough data to fit these models well?

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Simon Fraser University Department of Statistics and Actuarial Sciences Complexity Important elicitation problem How complex is the response surface y(x) ? How to build likely model complexity into design/analysis –GP models are very complex and hard to interpret –Even more challenging in calibration/assimilation problems

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Simon Fraser University Department of Statistics and Actuarial Sciences Complexity

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Simon Fraser University Department of Statistics and Actuarial Sciences Sample Size…Emulating a computer model

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Simon Fraser University Department of Statistics and Actuarial Sciences Simulation p= 27, n=50,100,200,300,500 Random design Symmetric LHS Predictions for 100 holdout x’s

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