(Problem 4: Votes are In)
(Problem 4: Votes are In)
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*[[Andrew Hermann 8.4_ECE302Fall2008sanghavi]]
 
*[[Andrew Hermann 8.4_ECE302Fall2008sanghavi]]
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[[Junzhe Geng 8.4_ECE302Fall2008sanghavi]]

Revision as of 16:42, 3 November 2008

Instructions

Homework 8 can be downloaded here on the ECE 302 course website.

Problem 1: Gone Fishin'

On average, it takes 1 hour to catch a fish.

  • (a) What is (an upper bound on) the probability that it will take 3 hours?
  • (b) Landis only has 2 hours to spend fishing. What is (an upper bound on) the probability he will go home fish-less?

Problem 2: Bounded Variance

Problem 3: "Bias" Estimate

  • (a) You have a coin of unknown bias. You flip it 10 times, and get TTHHTHTTHT as the sequence of outcomes. What is the maximum likelihood estimate of the bias (i.e. the probability, $ p $, of heads)?
  • (b) A friend has a coin of unknown bias. He flips it $ n $ times, and finds that $ k $ of them were heads. However, he neglects to record the exact sequence. What is the max-likelihood estimate for the bias in this case?

Problem 4: Votes are In

The election is over and the votes have been cast. If all the votes were counted, it would show that McCain has won Tippecanoe county. However, the Tippecanoe election officer is lazy however, and decides he is just going to count 1000 random votes. He does so, and finds 600 for Obama and 400 for McCain. He declares Obama has won Tippecanoe. What is (an upper bound on) such an event occuring? (Assume, as in class, that Tippecanoe has an infinite number of people.)

Hint: Let $ p $ be the true fraction of people that voted for Obama. We know that $ p < 1/2 $. Now find and upper bound on the event as a function of $ p $, and then maximize over $ p $.


Junzhe Geng 8.4_ECE302Fall2008sanghavi

Alumni Liaison

BSEE 2004, current Ph.D. student researching signal and image processing.

Landis Huffman