Thread: Exercise 1.10 View Single Post
#3
09-24-2015, 01:35 PM
 magdon RPI Join Date: Aug 2009 Location: Troy, NY, USA. Posts: 595
Re: Exercise 1.10

The problem asks you to compute P[|ν-μ| > ε] from your data for ε equal to (say) 0,0.01,0.02,0.03,....0.5
Now plot this computed probability for each value of epsilon versus epsilon.

Quote:
 Originally Posted by LambdaX I apologize for the previous format. I can't seem to find a way to edit or delete the thread. Here's a more readable version. What does it mean by the following? "plot estimates for P[|ν-μ| > ε] as a function of ε, together with the Hoeffding bound 2e^(-2(ε^2)N) (on the same graph)." Does this mean to plot P[|ν-μ| > ε] and 2e^(-2(ε^2)N) each as a separate graph? I can plot 2e^(-2(ε^2)N) as a function of ε easily, but how would I go about plotting P[|ν-μ| > ε]? Would I define a function that plots the likelihood that |ν-μ| > ε based on the input ε, using the data obtained in part b? Am I on the right track with this thinking? Also, is the book asking to plot a separate graph for each graph in b (i.e. ν_1 ν_rand, and ν_min), based on the distribution of ν for each?
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