The problem asks you to compute P[νμ > 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[νμ > 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[νμ > 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[νμ > ε]? Would I define a function that plots the likelihood that νμ > 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?
