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

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Presentation transcript:

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

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?

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?

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

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?)

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

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?

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

Simon Fraser University Department of Statistics and Actuarial Sciences Complexity

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

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