Exercise 1.10
I'm confused as to what part c of Exercise 1.10 is asking. What does it mean by the following?
HTML Code:
"plot estimates for P[νμ > ε] as a function of ε, together with the Hoeffding bound 2e<sup>2ε<sup>2</sup>N</sup> (on the same graph)." HTML Code:
Does this mean to plot P[νμ > ε] and 2e<sup>2ε<sup>2</sup>N</sup> each as a separate graph? I can plot 2e<sup>2ε<sup>2</sup>N</sup> 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? HTML Code:
Also, is the book asking to plot a separate graph for each graph in b (i.e. ν<sub>1</sub>, ν<sub>rand</sub>, and ν<sub>min</sub>), based on the distribution of ν for each? 
Re: Exercise 1.10
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? 
Re: Exercise 1.10
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:

All times are GMT 7. The time now is 01:36 PM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2020, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. AbuMostafa, Malik MagdonIsmail, and HsuanTien Lin, and participants in the Learning From Data MOOC by Yaser S. AbuMostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.