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WW1.05 - Optimal Learning with an Application to Characterizing Nano-Emulsion Stability 
Date/Time:
April 22, 2014   9:45am - 10:00am
 
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Scientists are often posed with the problem of finding a good choice of experimental parameters that optimize a particular quantity in the presence of ambiguity about the underlying system. This ambiguity arises from unknown physical constants, competing (and often poorly understood) mechanisms, and measurement or calibration error. Instead of an exhaustive and potentially expensive search over all such parameters, the method of Optimal Learning uses Bayesian statistics and heuristics to guide an experimenter through parameter space in an efficient manner, with the goal of learning about the underlying physical system through each experiment. We give an overview of the Optimal Learning method and the heuristics involved. We present how a meaningful baseline prior belief is elicited, using the domain knowledge of the experimenter. We then present a real-world application of the Optimal Learning technique to characterizing the stability of nano-emulsions stabilized by gold nanorods.
 


 
 
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