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Difference between revisions of "Aufgaben:Exercise 1.7Z: BARBARA Generator"

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{{quiz-Header|Buchseite=Stochastische Signaltheorie/Markovketten}}
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{{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Markov_Chains}}
  
[[File:P_ID454__Sto_Z_1_7.png|right|frame|BARBARA-Generator]]
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[[File:P_ID454__Sto_Z_1_7.png|right|frame|$\rm BARBARA$  Generator]]
Betrachtet wird hier ein ternärer Zufallsgenerator mit den Symbolen  A,  B  und  R, der durch eine homogene und stationäre Markovkette erster Ordnung beschrieben werden kann.
+
Here we consider a ternary random generator with symbols  A,  B  and  R,  which can be described by a homogeneous and stationary first order Markov chain.
  
*Die Übergangswahrscheinlichkeiten können dem skizzierten Markovdiagramm entnommen werden.  
+
*The transition probabilities can be taken from the sketched Markov diagram.
*Für die ersten drei Teilaufgaben soll stets  p=1/4  gelten.
+
*For the first three subtasks,  p=1/4  should always hold.
  
  
 
+
Hints:
 
+
*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Markov_Chains|Markov Chains]].
 
 
 
 
''Hinweis:''
 
*Die Aufgabe gehört zum  Kapitel  [[Theory_of_Stochastic_Signals/Markovketten|Markovketten]].
 
 
   
 
   
  
  
===Fragebogen===
+
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welche der nachfolgenden Aussagen sind zutreffend?
+
{Which of the following statements are true?
 
|type="[]"}
 
|type="[]"}
- Die Werte von&nbsp; p>0&nbsp; und&nbsp; q<1&nbsp; sind weitgehend frei wählbar.
+
- The values of&nbsp; p>0&nbsp; and&nbsp; q<1&nbsp; are largely arbitrary.
+ Für die Übergangswahrscheinlichkeiten muss gelten: &nbsp; p+q=1.
+
+ For the transition probabilities,&nbsp; the following must hold: &nbsp; p+q=1.
+ Alle Symbole haben gleiche ergodische Wahrscheinlichkeiten.
+
+ All symbols have equal ergodic probabilities.
- Es gilt hier:&nbsp; Pr(A)=1/2,Pr(B)=1/3,Pr(R)=1/6.
+
- It holds here:&nbsp; Pr(A)=1/2,Pr(B)=1/3,Pr(R)=1/6.
  
{Wie groß sind die bedingten Wahrscheinlichkeiten&nbsp; pA,&nbsp; pB&nbsp; und&nbsp; pC, dass zu den Zeiten zwischen&nbsp; ν+1&nbsp; und&nbsp; ν+7&nbsp; die Sequenz&nbsp; BARBARA&nbsp; ausgegeben wird, <br>wenn man sich zum Zeitpunkt&nbsp; ν&nbsp; im Zustand&nbsp; A,&nbsp; B&nbsp; bzw.&nbsp; R&nbsp; befindet?&nbsp; Es gelte&nbsp; p = 1/4.
+
{What are the conditional probabilities&nbsp; p_{\rm A},&nbsp; p_{\rm B}&nbsp; and&nbsp; p_{\rm C} that at times between&nbsp; ν+1&nbsp; and&nbsp; ν+7&nbsp; the sequence&nbsp; "$\rm BARBARA$"&nbsp; is output, <br>if one is at time&nbsp; ν&nbsp; in state&nbsp; A,&nbsp; B&nbsp; or&nbsp; R,&nbsp; respectively?&nbsp; Let&nbsp; p = 1/4.
 
|type="{}"}
 
|type="{}"}
 
p_{\rm A} \ = \   { 0.549 3% } \ \cdot 10^{-3}  
 
p_{\rm A} \ = \   { 0.549 3% } \ \cdot 10^{-3}  
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p_{\rm C} \ = \ { 0.183 3% } \ \cdot 10^{-3}  
 
p_{\rm C} \ = \ { 0.183 3% } \ \cdot 10^{-3}  
  
{Wie groß ist die Wahrscheinlichkeit insgesamt, dass der Generator zu sieben aufeinanderfolgenden Zeitpunkten die Sequenz&nbsp; BARBARA&nbsp; ausgibt?<br>  Es gelte weiter&nbsp; $p = 1/4.$
+
{What is the overall probability that the generator outputs the sequence&nbsp; "$\rm BARBARA$"?&nbsp;   Let&nbsp; $p = 1/4$ continue to hold.
 
|type="{}"}
 
|type="{}"}
{\rm Pr}(BARBARA)\ = \ { 0.244 3% } \ \cdot 10^{-3}
+
${\rm Pr}(\rm BARBARA)\ = \ { 0.244 3% } \ \cdot 10^{-3}$
  
{Wie ist der Parameter&nbsp; p_{\rm opt}&nbsp; zu wählen, damit&nbsp; {\rm Pr}(BARBARA)&nbsp; möglichst groß wird?  <br>Welche Wahrscheinlichkeit ergibt sich damit für&nbsp; BARBARA?
+
{How should the parameter&nbsp; p_{\rm opt}&nbsp; be chosen to make&nbsp; ${\rm Pr}(\rm BARBARA)$&nbsp; as large as possible?  <br>What is the resulting probability for&nbsp; "$\rm BARBARA$"?
 
|type="{}"}
 
|type="{}"}
 
p_{\rm opt} \ = \   { 0.8333 3% }
 
p_{\rm opt} \ = \   { 0.8333 3% }
p = p_{\rm opt}\hspace{-0.1cm}: \hspace{0.3cm}{\rm Pr}(BARBARA)\ = \ { 22 3% } \ \cdot 10^{-3}
+
$p = p_{\rm opt}\hspace{-0.1cm}: \hspace{0.3cm}{\rm Pr}(\rm BARBARA)\ = \ { 22 3% } \ \cdot 10^{-3}$
  
 
</quiz>
 
</quiz>
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===Musterlösung===
 
===Musterlösung===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Richtig sind <u>der zweite und der dritte Lösungsvorschlag</u>:
+
'''(1)'''&nbsp; Correct are&nbsp; <u>the second and third suggested solutions</u>:
*Die Summe aller abgehenden Pfeile muss immer&nbsp; 1&nbsp; sein. Deshalb gilt&nbsp; q = 1 - p.  
+
*The sum of all outgoing arrows must always be&nbsp; 1.&nbsp; Therefore&nbsp; q = 1 - p holds.  
*Aufgrund der Symmetrie des Markovdiagramms sind die ergodischen Wahrscheinlichkeiten alle gleich:
+
*Because of the symmetry of the Markov diagram,&nbsp; the ergodic probabilities are all equal:
 
:{\rm Pr}(A) ={\rm Pr}(B) ={\rm Pr}(R) = 1/3.
 
:{\rm Pr}(A) ={\rm Pr}(B) ={\rm Pr}(R) = 1/3.
  
  
 
+
'''(2)'''&nbsp; If one is in the state&nbsp; B&nbsp; at the starting time&nbsp; $\nu=0$,&nbsp; because of&nbsp; {\rm Pr}(B\hspace{0.05cm}|\hspace{0.05cm}B) = 0&nbsp; the state&nbsp; B&nbsp; is not possible at time&nbsp; \nu=1.  
'''(2)'''&nbsp; Wenn man zum Startzeitpunkt&nbsp; \nu = 0&nbsp; im Zustand&nbsp; B&nbsp; ist, ist für den Zeitpunkt&nbsp; $\nu=1$&nbsp; wegen&nbsp; {\rm Pr}(B\hspace{0.05cm}|\hspace{0.05cm}B) = 0 der Zustand&nbsp; B&nbsp; nicht möglich.  
+
*One fails here already with the initial letter&nbsp; B:  
*Man scheitert hier bereits beim Anfangsbuchstaben B:  
 
 
:p_{\rm B} \; \underline{ =0}.
 
:p_{\rm B} \; \underline{ =0}.
  
*F&uuml;r die Berechnung von&nbsp; p_{\rm A}&nbsp; ist zu beachten: &nbsp; Ausgehend von&nbsp; A&nbsp; geht man im Markovdiagramm zun&auml;chst zu&nbsp; B&nbsp; (mit der Wahrscheinlichkeit q), dann f&uuml;nfmal im Uhrzeigersinn&nbsp; (jeweils mit der Wahrscheinlichkeit p)&nbsp; und schlie&szlig;lich noch von&nbsp; R&nbsp; nach&nbsp; A&nbsp; (mit der Wahrscheinlichkeit&nbsp; q).&nbsp; Das bedeutet:
+
*For the calculation of&nbsp; p_{\rm A}&nbsp; it should be noted: &nbsp; Starting from&nbsp; A&nbsp; one goes in the Markov diagram first to&nbsp; B&nbsp; (with probability q),&nbsp; then five times clockwise&nbsp; (each time with probability p)&nbsp; and finally from&nbsp; R&nbsp; to&nbsp; A&nbsp; (with probability&nbsp; q). &nbsp; Meaning:
:$$p_{\rm A} = q^2 \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 = 3^2 / 4^7 \hspace{0.15cm}\underline {\approx 0.549 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.$$
+
:p_{\rm A} = q^2 \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 = 3^2 / 4^7 \hspace{0.15cm}\underline {\approx 0.549 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.
*In &auml;hnlicher Weise erh&auml;lt man:
+
*In a similar way,&nbsp; one obtains:
:$$p_{\rm R} = q \hspace{0.05cm}\cdot \hspace{0.05cm} p^6 = 3 / 4^7 \hspace{0.15cm}\underline {\approx 0.183 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.$$
+
:p_{\rm R} = q \hspace{0.05cm}\cdot \hspace{0.05cm} p^6 = 3 / 4^7 \hspace{0.15cm}\underline {\approx 0.183 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.
 
 
 
 
  
'''(3)'''&nbsp; Durch Mittelung &uuml;ber die bedingten Wahrscheinlichkeiten erh&auml;lt man:
 
:{\rm Pr}(BARBARA) = p_{\rm A}  \hspace{0.05cm}\cdot  \hspace{0.05cm} {\rm Pr}(A) \hspace{0.1cm} + \hspace{0.1cm}p_{\rm B}  \hspace{0.05cm}\cdot  \hspace{0.05cm} {\rm Pr}(B) \hspace{0.1cm} + \hspace{0.1cm}p_{\rm R}  \hspace{0.05cm}\cdot  \hspace{0.05cm} {\rm Pr}(R).
 
Dies f&uuml;hrt zum Ergebnis:
 
:$${\rm Pr}(BARBARA) =  {1}/{3} \cdot \left( q^2 \hspace{0.05cm}\cdot  \hspace{0.05cm} p^5  \hspace{0.1cm} +\hspace{0.1cm}0  \hspace{0.1cm} +\hspace{0.1cm}q \hspace{0.05cm}\cdot  \hspace{0.05cm} p^6  \right)
 
= \frac{q \hspace{0.05cm}\cdot  \hspace{0.05cm} p^5 }{3} \cdot (p+q)
 
= \hspace{-0.15cm} \frac{q \hspace{0.05cm}\cdot  \hspace{0.05cm} p^5 }{3}
 
\hspace{0.15cm}\underline { \approx 0.244  \hspace{0.05cm}\cdot  \hspace{0.05cm} 10^{-3}}.$$
 
  
 +
'''(3)'''&nbsp; By averaging over the conditional probabilities we obtain:
 +
:{\rm Pr}(\rm BARBARA) = p_{\rm A}  \hspace{0.05cm}\cdot \hspace{0.05cm} {\rm Pr}(A) \hspace{0.1cm} + \hspace{0.1cm}p_{\rm B}  \hspace{0.05cm}\cdot \hspace{0.05cm} {\rm Pr}(B) \hspace{0.1cm} + \hspace{0.1cm}p_{\rm R}  \hspace{0.05cm}\cdot \hspace{0.05cm} {\rm Pr}(R).
 +
*This leads to the result:
 +
:$${\rm Pr}(\rm BARBARA) = {1}/{3} \cdot \left( q^2 \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 \hspace{0.1cm} +\hspace{0.1cm}0 \hspace{0.1cm} +\hspace{0.1cm}q \hspace{0.05cm}\cdot \hspace{0.05cm} p^6 \right)
 +
= \frac{q \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 }{3} \cdot{p+q}
 +
= \hspace{-0.15cm} \frac{q \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 }{3}
 +
\hspace{0.15cm}\underline { \approx 0.244 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.$$
  
'''(4)'''&nbsp; Die im Punkt&nbsp; '''(3)'''&nbsp; berechnete Wahrscheinlichkeit lautet&nbsp; p^5 \cdot (1-p)/3, wobei&nbsp; q= 1-p&nbsp; berücksichtigt ist.  
+
'''(4)'''&nbsp; The probability calculated in&nbsp; '''(3)'''&nbsp; is&nbsp; p^5 \cdot (1-p)/3,&nbsp; where&nbsp; q= 1-p&nbsp; is considered.  
  
*Durch Nullsetzen des Differentials erh&auml;lt man die Bestimmungsgleichung:
+
*By setting the differential to zero, we obtain the governing equation:
:$$5 \cdot p^4 - 6 \cdot p^5 = 0 \hspace{0.5cm} \Rightarrow \hspace{0.5cm} p_{\rm opt} = 5/6 \hspace{0.15cm}\underline { \approx \rm 0.833}.$$
+
:5 \cdot p^4 - 6 \cdot p^5 = 0 \hspace{0.5cm} \Rightarrow \hspace{0.5cm} p_{\rm opt} = 5/6 \hspace{0.15cm}\underline { \approx \rm 0.833}.
*Damit ergibt sich ein gegen&uuml;ber der Teilaufgabe&nbsp; '''(3)'''&nbsp; etwa um den Faktor&nbsp; 90&nbsp; gr&ouml;&szlig;erer Wert:  
+
*This results in a value that is larger than the subtask&nbsp; '''(3)'''&nbsp; by a factor&nbsp; 90&nbsp; approximately:  
:$${\rm Pr}(BARBARA)   \hspace{0.15cm}\underline { \approx 22 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.$$
+
:$${\rm Pr}\rm (BARBARA) \hspace{0.15cm}\underline { \approx 22 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.$$
  
 
{{ML-Fuß}}
 
{{ML-Fuß}}
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[[Category:Aufgaben zu Stochastische Signaltheorie|^1.4 Markovketten
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[[Category:Theory of Stochastic Signals: Exercises|^1.4 Markov Chains
 
^]]
 
^]]

Latest revision as of 18:29, 2 December 2021

\rm BARBARA  Generator

Here we consider a ternary random generator with symbols  AB  and  R,  which can be described by a homogeneous and stationary first order Markov chain.

  • The transition probabilities can be taken from the sketched Markov diagram.
  • For the first three subtasks,  p = 1/4  should always hold.


Hints:


Questions

1

Which of the following statements are true?

The values of  p > 0  and  q < 1  are largely arbitrary.
For the transition probabilities,  the following must hold:   p + q = 1.
All symbols have equal ergodic probabilities.
It holds here:  {\rm Pr}(A) = 1/2, \; {\rm Pr}(B) = 1/3, \; {\rm Pr}(R) = 1/6.

2

What are the conditional probabilities  p_{\rm A}p_{\rm B}  and  p_{\rm C} that at times between  ν+1  and  ν+7  the sequence  "\rm BARBARA"  is output,
if one is at time  ν  in state  AB  or  R,  respectively?  Let  p = 1/4.

p_{\rm A} \ = \

\ \cdot 10^{-3}
p_{\rm B} \ = \

\ \cdot 10^{-3}
p_{\rm C} \ = \

\ \cdot 10^{-3}

3

What is the overall probability that the generator outputs the sequence  "\rm BARBARA"?  Let  p = 1/4 continue to hold.

{\rm Pr}(\rm BARBARA)\ = \

\ \cdot 10^{-3}

4

How should the parameter  p_{\rm opt}  be chosen to make  {\rm Pr}(\rm BARBARA)  as large as possible?
What is the resulting probability for  "\rm BARBARA"?

p_{\rm opt} \ = \

p = p_{\rm opt}\hspace{-0.1cm}: \hspace{0.3cm}{\rm Pr}(\rm BARBARA)\ = \

\ \cdot 10^{-3}


Musterlösung

(1)  Correct are  the second and third suggested solutions:

  • The sum of all outgoing arrows must always be  1.  Therefore  q = 1 - p holds.
  • Because of the symmetry of the Markov diagram,  the ergodic probabilities are all equal:
{\rm Pr}(A) ={\rm Pr}(B) ={\rm Pr}(R) = 1/3.


(2)  If one is in the state  B  at the starting time  \nu=0,  because of  {\rm Pr}(B\hspace{0.05cm}|\hspace{0.05cm}B) = 0  the state  B  is not possible at time  \nu=1.

  • One fails here already with the initial letter  B:
p_{\rm B} \; \underline{ =0}.
  • For the calculation of  p_{\rm A}  it should be noted:   Starting from  A  one goes in the Markov diagram first to  B  (with probability q),  then five times clockwise  (each time with probability p)  and finally from  R  to  A  (with probability  q).   Meaning:
p_{\rm A} = q^2 \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 = 3^2 / 4^7 \hspace{0.15cm}\underline {\approx 0.549 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.
  • In a similar way,  one obtains:
p_{\rm R} = q \hspace{0.05cm}\cdot \hspace{0.05cm} p^6 = 3 / 4^7 \hspace{0.15cm}\underline {\approx 0.183 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.


(3)  By averaging over the conditional probabilities we obtain:

{\rm Pr}(\rm BARBARA) = p_{\rm A} \hspace{0.05cm}\cdot \hspace{0.05cm} {\rm Pr}(A) \hspace{0.1cm} + \hspace{0.1cm}p_{\rm B} \hspace{0.05cm}\cdot \hspace{0.05cm} {\rm Pr}(B) \hspace{0.1cm} + \hspace{0.1cm}p_{\rm R} \hspace{0.05cm}\cdot \hspace{0.05cm} {\rm Pr}(R).
  • This leads to the result:
{\rm Pr}(\rm BARBARA) = {1}/{3} \cdot \left( q^2 \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 \hspace{0.1cm} +\hspace{0.1cm}0 \hspace{0.1cm} +\hspace{0.1cm}q \hspace{0.05cm}\cdot \hspace{0.05cm} p^6 \right) = \frac{q \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 }{3} \cdot{p+q} = \hspace{-0.15cm} \frac{q \hspace{0.05cm}\cdot \hspace{0.05cm} p^5 }{3} \hspace{0.15cm}\underline { \approx 0.244 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.

(4)  The probability calculated in  (3)  is  p^5 \cdot (1-p)/3,  where  q= 1-p  is considered.

  • By setting the differential to zero, we obtain the governing equation:
5 \cdot p^4 - 6 \cdot p^5 = 0 \hspace{0.5cm} \Rightarrow \hspace{0.5cm} p_{\rm opt} = 5/6 \hspace{0.15cm}\underline { \approx \rm 0.833}.
  • This results in a value that is larger than the subtask  (3)  by a factor  90  approximately:
{\rm Pr}\rm (BARBARA) \hspace{0.15cm}\underline { \approx 22 \hspace{0.05cm}\cdot \hspace{0.05cm} 10^{-3}}.