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  #1  
Old 04-12-2010, 11:45 AM
Corey Bryant Corey Bryant is offline
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Default Corey Bryant Term Project

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Old 04-13-2010, 12:32 PM
wpress wpress is offline
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Seems to me that this is basically about the estimation of a parameter from a data set. I wonder,
1. Does your synthetic data have noise? How is the noise generated? Which methods do better is going to depend strongly on the noise model, not just the physics model.
2. It isn't clear to me that all of your different calibration methods are actually different. Can you explain (analytically) what the differences ought to be? I don't think it makes sense to compute the same estimator in two different ways if it is obvious that one computational method is better.
Good luck!
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  #3  
Old 05-05-2010, 02:49 PM
Corey Bryant Corey Bryant is offline
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Default Lecture slides on model selection

The second option to do lecture slides instead.

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  #4  
Old 05-07-2010, 02:09 PM
wpress wpress is offline
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Good summary of available techniques, though it left me wishing for more depth in the section that compares them.

In slide 11, I think the interesting step (how to get from the first equation to the second) should be sketched, not just reference.

In the AIC slides, it could be made clearer what the loss function is, e.g., by giving examples.
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