#1




Q1 and Q2
I am a bit confused about these two questions. Without getting the actual answers, any insight would be appreciated.
First, with regards to Q1; I have run the experiment many times, and I don't get any of the suggested answers. This is my code, v_1 = 0; v_rand = 0; v_min = 0; vmins = zeros(100000,1); for i=1:100000 experiment = zeros (10,1000); vs = zeros (1,1000); experiment = randi([0 1],10,1000); % Do the experiment vs = sum(experiment)/10; % compute frequencies v_1 = v_1 + vs(1); v_rand = v_rand + vs(randi(1000)); vmins(i) = min(vs); % find v_min v_min = v_min + vmins(i); end v_1 = v_1/100000 v_rand = v_rand/100000 v_min = v_min/100000 With that code I get a v_min which is neither of the suggested. I don't see what I am doing wrong. Regarding Q2; there is another thread on the matter http://book.caltech.edu/bookforum/showthread.php?t=880 but I am still unclear. I kind of believe what is the right answer, but I am confused about the wording of the question. Hoeffding’s inequality requires a particular setup to be useful from a quantitative point of view: N (number of experiments, in Q1 that's 10) and epsilon (the tolerance, which we haven't been given). Without a required epsilon, we cannot reach any conclusions from a quantitative point of view. My feeling is that Q2 is a question independent of any N (thus, forget about 10) or epsilon (thus not required). Something along the lines: "suppose we have 1000 thousand identical coins and we wanted to estimate the bias of any the coins. We choose c_1, c_rand and c_min as explained in Q1. For which coin (or coins) could we use Hoeffding’s inequality to get a fair estimate of the number of samples required to have an estimate of the bias for a given tolerance? For example, plugging in N = 10000, epsilon = 0.01, 2*exp(2*(0.01)^2*100000) = 4.1223e009, which is pretty good. Which coin(s) will give us the coin bias with a probability 1  4.1223e009?" If Q2 is about what goes in between "", then I know the answer. If not, I am confused. 
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