(New page: Category: ECE Category: HKN Category: QE Category: Automatic Controls Category: Linear Systems =Problem 1= X and Y are iid <math> P(X=i) = P(Y=i) = \frac {1}{2^i}...)
 
 
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[[Category: Automatic Controls]]
 
[[Category: Automatic Controls]]
 
[[Category: Linear Systems]]
 
[[Category: Linear Systems]]
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[[Category: August]]
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[[Category: 2007]]
  
=Problem 1=
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=[[Automatic_Controls:Linear_Systems_(HKNQE_August_2007)_Problem_1|Problem 1]]=
X and Y are iid
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{{:Automatic_Controls:Linear_Systems_(HKNQE_August_2007)_Problem_1}}
  
    <math> P(X=i) = P(Y=i) = \frac {1}{2^i}\ ,i = 1,2,3,... </math>
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=[[Automatic_Controls:Linear_Systems_(HKNQE_August_2007)_Problem_2|Problem 2]]=
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{{:Automatic_Controls:Linear_Systems_(HKNQE_August_2007)_Problem_2}}
  
==Part A==
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=[[Automatic_Controls:Linear_Systems_(HKNQE_August_2007)_Problem_3|Problem 3]]=
    Find <math> P(min(X,Y)=k)\ </math>
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{{:Automatic_Controls:Linear_Systems_(HKNQE_August_2007)_Problem_3}}
  
    Let <math> Z = min(X,Y)\ </math>
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=[[Automatic_Controls:Linear_Systems_(HKNQE_August_2007)_Problem_4|Problem 4]]=
 
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{{:Automatic_Controls:Linear_Systems_(HKNQE_August_2007)_Problem_4}}
Then finding the pmf of Z uses the fact that X and Y are iid
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    <math> P(Z=k) = P(X \ge k,Y \ge k) = P(X \ge k)P(Y \ge k) = P(X \ge k)^2 </math>
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    <math> P(Z=k) = \left ( \sum_{i=k}^N \frac {1}{2^i} \right )^2 = \left ( \frac {1}{2^k} \right )^2 = \frac {1}{4^k} </math>
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Latest revision as of 10:42, 3 December 2008


Problem 1

X and Y are iid

   $  P(X=i) = P(Y=i) = \frac {1}{2^i}\ ,i = 1,2,3,...  $

Part A

  • Find $ P(min(X,Y)=k)\ $
   Let $  Z = min(X,Y)\  $
   Then finding the pmf of Z uses the fact that X and Y are iid
       $  P(Z=k) = P(X \ge k,Y \ge k) = P(X \ge k)P(Y \ge k) = P(X \ge k)^2  $
       $  P(Z=k) = \left ( \sum_{i=k}^N \frac {1}{2^i} \right )^2 = \left ( \frac {1}{2^k} \right )^2 = \frac {1}{4^k}  $

Part B

  • Find $ P(X=Y)\ $
   Noting that X and Y are iid and summing across all possible i,
       $  P(X=Y) = \sum_{i=1}^\infty P(X=i, Y=i) = \sum_{i=1}^\infty P(X=i)P(Y=i) = \sum_{i=1}^\infty \frac {1}{4^i} = \frac {1}{3}  $

Part C

  • Find $ P(Y>X)\ $
   Again noting that X and Y are iid and summing across all possible i,
       $  P(Y>X) = \sum_{i=1}^\infty P(Y>i, X=i) = \sum_{i=1}^\infty P(Y>i)P(x=i) $
   Next, find $  P(Y<i)\  $
       $  P(Y>i) = 1 - P(Y \le i)  $
       $  P(Y \le i) = \sum_{i=1}^\infty \frac {1}{2^i} = 1 + \frac {1}{2^i}  $
     $  \therefore P(Y>i) = \frac {1}{2^i}  $
   Plugging this result back into the original expression yields
       $  P(Y<X) = \sum_{i=1}^\infty \frac {1}{4^i} = \frac {1}{3}  $

Part D

  • Find $ P(Y=kX)\ $
   Noting that X and Y are iid and summing over all possible combinations one arrives at
       $  P(Y=kX) = \sum_{i=1}^\infty i = 1^\infty P(Y=ki, X=i) = \sum_{i=1}^\infty P(Y=ki)P(X=i)  $
   Thus,
       $  P(Y=kX) = \sum_{i=1}^\infty \frac {1}{2^{ki}} \frac {1}{2^i} = \sum_{i=1}^\infty \frac {1}{2^{(k+1)i}} = \frac {1}{2^{(k+1)}-1}  $

Problem 2

Automatic Controls:Linear Systems (HKNQE August 2007) Problem 2

Problem 3

Automatic Controls:Linear Systems (HKNQE August 2007) Problem 3

Problem 4

Automatic Controls:Linear Systems (HKNQE August 2007) Problem 4

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva