Difference between revisions of "Aufgaben:Exercise 1.7: Ternary Markov Chain"

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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_ID453__Sto_A_1_7.png|right|frame|Ternäre Markovkette]]
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[[File:P_ID453__Sto_A_1_7.png|right|frame|Ternary Markov chain]]
Wir betrachten eine Markovkette mit den drei möglichen Ereignissen  $A$,  $B$  und  $C$:  
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We consider a Markov chain with three possible events  $A$,  $B$,  $C$:  
*Die Übergangswahrscheinlichkeiten sind der Grafik zu entnehmen.  
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*The transition probabilities are shown in the graph.
*Ein Übergang von  $A$  nach  $C$  und umgekehrt ist somit nicht möglich:
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*Thus,  a transition from  $A$  to  $C$  and vice versa is not possible.
  
 
:$$p_\text{AC} = p_\text{CA} = 0.$$  
 
:$$p_\text{AC} = p_\text{CA} = 0.$$  
  
Die drei Ereigniswahrscheinlichkeiten zum Startzeitpunkt  $\nu = 0$  sind wie folgt gegeben:
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The three event probabilities at the starting time  $(\nu = 0)$  are given as follows:
 
   
 
   
 
:$${\rm Pr}(A_0) = 0,$$
 
:$${\rm Pr}(A_0) = 0,$$
 
 
:$${\rm Pr}(B_0) = 1,$$
 
:$${\rm Pr}(B_0) = 1,$$
 
 
:$${\rm Pr}(C_0) = 0.$$
 
:$${\rm Pr}(C_0) = 0.$$
 
 
 
  
  
  
 
   
 
   
''Hinweise:''
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Hints:  
*Die Aufgabe gehört zum  Kapitel  [[Theory_of_Stochastic_Signals/Markovketten|Markovketten]].
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*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Markov_Chains|Markov Chains]].
*Insbesondere wird auf die Seite  [[Theory_of_Stochastic_Signals/Markovketten#Matrix-Vektordarstellung|Matrix-Vektordarstellung]]  Bezug genommen.
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*In particular,  reference is made to the page  [[Theory_of_Stochastic_Signals/Markov_Chains#Matrix_vector_representation|Matrix vector representation]].
 
   
 
   
  
  
===Fragebogen===
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===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Geben Sie die Übergangsmatrix&nbsp; ${\mathbf{P}}$&nbsp; und die Übergangswahrscheinlichkeiten&nbsp; $p_\text{AA}$,&nbsp; $p_\text{BB}$&nbsp; und&nbsp; $p_\text{CC}$ an.
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{Give the transition matrix&nbsp; ${\mathbf{P}}$&nbsp; and the transition probabilities&nbsp; $p_\text{AA}$,&nbsp; $p_\text{BB}$&nbsp; and&nbsp; $p_\text{CC}$.
 
|type="{}"}
 
|type="{}"}
 
$p_\text{AA} \ = \ $ { 0.25 3% }
 
$p_\text{AA} \ = \ $ { 0.25 3% }
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$p_\text{CC} \ = \ $ { 0.75 3% }
 
$p_\text{CC} \ = \ $ { 0.75 3% }
  
{Berechnen Sie die Ereigniswahrscheinlichkeiten zum Zeitpunkt&nbsp; $\nu = 1$, insbesondere
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{Calculate the event probabilities at time&nbsp; $\nu = 1$,&nbsp; in particular
 
|type="{}"}
 
|type="{}"}
 
${\rm Pr}(A_1) \ = \ $ { 0.75 3% }
 
${\rm Pr}(A_1) \ = \ $ { 0.75 3% }
  
{Berechnen Sie die Ereigniswahrscheinlichkeiten zum Zeitpunkt&nbsp; $\nu = 2$, insbesondere
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{Calculate the event probabilities at time&nbsp; $\nu = 2$,&nbsp; in particular
 
|type="{}"}
 
|type="{}"}
 
${\rm Pr}(A_2) \ =  \ ${ 0.1875 3% }
 
${\rm Pr}(A_2) \ =  \ ${ 0.1875 3% }
  
{Welche Wahrscheinlichkeiten werden sich sehr lange nach Einschalten der Markovkette einstellen&nbsp; $(ν \rightarrow \infty)$? <br>Wie groß ist insbesondere die ergodische Wahrscheinlichkeit&nbsp; ${\rm Pr}(A)$?
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{What probabilities will occur very long after the Markov chain is turned on&nbsp; $(ν \rightarrow \infty)$? <br>In particular, what is the ergodic probability&nbsp; ${\rm Pr}(A)$?
 
|type="{}"}
 
|type="{}"}
 
${\rm Pr}(A) \ = \ $ { 0.333 3% }
 
${\rm Pr}(A) \ = \ $ { 0.333 3% }
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</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Allgemein bzw. in diesem Sonderfall muss gelten:
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'''(1)'''&nbsp; In general&nbsp; (or in this special case)&nbsp; it must hold:
 
:$$p_{\rm AA} = 1 - p_{\rm AB} - p_{\rm AC} \hspace{0.5cm} \Rightarrow  \hspace{0.5cm} p_{\rm AA} = 1 - 0.75 -0 \hspace{0.15cm}\underline {= 0.25},$$
 
:$$p_{\rm AA} = 1 - p_{\rm AB} - p_{\rm AC} \hspace{0.5cm} \Rightarrow  \hspace{0.5cm} p_{\rm AA} = 1 - 0.75 -0 \hspace{0.15cm}\underline {= 0.25},$$
 
:$$p_{\rm BB} = 1 - p_{\rm BA} - p_{\rm BC} \hspace{0.5cm} \Rightarrow  \hspace{0.5cm} p_{\rm BB} = 1 - 0.75 -0.25 \hspace{0.15cm}\underline {= 0},$$
 
:$$p_{\rm BB} = 1 - p_{\rm BA} - p_{\rm BC} \hspace{0.5cm} \Rightarrow  \hspace{0.5cm} p_{\rm BB} = 1 - 0.75 -0.25 \hspace{0.15cm}\underline {= 0},$$
 
:$$p_{\rm CC} = 1 - p_{\rm CA} - p_{\rm CB} \hspace{0.5cm} \Rightarrow  \hspace{0.5cm} p_{\rm CC} = 1 - 0 - 0.25 \hspace{0.15cm}\underline {= 0.75}.$$
 
:$$p_{\rm CC} = 1 - p_{\rm CA} - p_{\rm CB} \hspace{0.5cm} \Rightarrow  \hspace{0.5cm} p_{\rm CC} = 1 - 0 - 0.25 \hspace{0.15cm}\underline {= 0.75}.$$
*Damit lautet die &Uuml;bergangsmatrix:
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*Thus,&nbsp; the transition matrix is:
 
:$${\mathbf{P}} = \left[ \begin{array}{ccc} 1/4 & 3/4 & 0 \\ 3/4 & 0 & 1/4  \\ 0 & 1/4 & 3/4  \end{array} \right] .$$
 
:$${\mathbf{P}} = \left[ \begin{array}{ccc} 1/4 & 3/4 & 0 \\ 3/4 & 0 & 1/4  \\ 0 & 1/4 & 3/4  \end{array} \right] .$$
  
  
  
'''(2)'''&nbsp; Wegen&nbsp; ${\rm Pr}(B_0) = 1$&nbsp; und&nbsp; $p_\text{BB}  = 0$&nbsp; kann zum Zeitpunkt&nbsp; $\nu = 1$&nbsp; das Ereignis&nbsp; $B$&nbsp; nicht auftreten und&nbsp; $A$&nbsp; ist sehr viel wahrscheinlicher als&nbsp; $C$:
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'''(2)'''&nbsp; Because of&nbsp; ${\rm Pr}(B_0) = 1$&nbsp; and&nbsp; $p_\text{BB}  = 0$,&nbsp; at time&nbsp; $\nu = 1$&nbsp; the event&nbsp; $B$&nbsp; cannot occur and&nbsp; $A$&nbsp; is much more probable than&nbsp; $C$:
 
:$$\hspace{0.15cm}\underline {{\rm Pr}(A_1) = 0.75}; \hspace{0.5cm} {\rm Pr}(B_1) = 0; \hspace{0.5cm}{\rm Pr}(C_1) = 0.25.$$
 
:$$\hspace{0.15cm}\underline {{\rm Pr}(A_1) = 0.75}; \hspace{0.5cm} {\rm Pr}(B_1) = 0; \hspace{0.5cm}{\rm Pr}(C_1) = 0.25.$$
*Zum gleichen Ergebnis kommt man durch Anwendung der Vektor-Matrixdarstellung.
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*The same result is obtained by applying the vector matrix representation.
  
  
  
'''(3)'''&nbsp; F&uuml;r den Wahrscheinlichkeitsvektor zum Zeitpunkt&nbsp; $\nu = 2$&nbsp; gilt:
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'''(3)'''&nbsp; For the probability vector at time&nbsp; $\nu = 2$&nbsp; holds:
 
:$${\mathbf{p}^{(\nu = 2)}} = {\mathbf{P}}^{\rm T} \cdot {\mathbf{p}^{(\nu =1 )}}= \left[ \begin{array}{ccc} 1/4 & 3/4&  0 \\ 3/4 & 0 & 1/4 \\ 0& 1/4& 3/4  \end{array} \right] \left[ \begin{array}{c} 3/4 \\ 0  \\ 1/4  \end{array} \right] = \left[ \begin{array}{c} 3/16 \\ 10/16  \\ 3/16 \end{array} \right] .$$
 
:$${\mathbf{p}^{(\nu = 2)}} = {\mathbf{P}}^{\rm T} \cdot {\mathbf{p}^{(\nu =1 )}}= \left[ \begin{array}{ccc} 1/4 & 3/4&  0 \\ 3/4 & 0 & 1/4 \\ 0& 1/4& 3/4  \end{array} \right] \left[ \begin{array}{c} 3/4 \\ 0  \\ 1/4  \end{array} \right] = \left[ \begin{array}{c} 3/16 \\ 10/16  \\ 3/16 \end{array} \right] .$$
*Damit ist die Ereigniswahrscheinlichkeit&nbsp; ${\rm Pr}(A_2) = 3/16\hspace{0.15cm}\underline {=  0.1875}$.
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*Thus,&nbsp; the event probability is&nbsp; ${\rm Pr}(A_2) = 3/16\hspace{0.15cm}\underline {=  0.1875}$.
  
  
  
'''(4)'''&nbsp; Zur Lösung dieser Aufgabe sollen verschiedene M&ouml;glichkeiten angegeben werden.  
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'''(4)'''&nbsp; To solve this problem,&nbsp; several possibilities should be given.  
*Zum einen das L&ouml;sen eines Gleichungssystems mit drei Unbekannten:
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*One is to solve a system of equations with three unknowns:
 
:$${\rm Pr}(A) = 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(A)  \hspace{0.1cm} + \hspace{0.1cm} 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(B),$$
 
:$${\rm Pr}(A) = 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(A)  \hspace{0.1cm} + \hspace{0.1cm} 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(B),$$
 
:$${\rm Pr}(B) = 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(A)  \hspace{2.8cm} + \hspace{0.1cm} 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(C),$$
 
:$${\rm Pr}(B) = 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(A)  \hspace{2.8cm} + \hspace{0.1cm} 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(C),$$
 
:$${\rm Pr}(C) = \hspace{2.8cm} 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(B)  \hspace{0.1cm} + \hspace{0.1cm} 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(C).$$
 
:$${\rm Pr}(C) = \hspace{2.8cm} 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(B)  \hspace{0.1cm} + \hspace{0.1cm} 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(C).$$
:Aus der ersten Gleichung erh&auml;lt man ${\rm Pr}(B) = {\rm Pr}(A)$, aus der letzten ${\rm Pr}(C) = {\rm Pr}(A)$. Da die Summe aller Wahrscheinlichkeiten gleich $1$ ist, folgt $ {\rm Pr}(A) = {\rm Pr}(B) = {\rm Pr}(C) = 1/3 \hspace{0.15cm}\underline {\approx  0.333}$.
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:From the first equation we get&nbsp; ${\rm Pr}(B) = {\rm Pr}(A)$,&nbsp; from the last&nbsp; ${\rm Pr}(C) = {\rm Pr}(A)$.&nbsp; Since the sum of all probabilities is equal to&nbsp; $1$,&nbsp; it follows 
*Zum gleichen Ergebnis kommt man durch Analyse der &Uuml;bergangsmatrix.&nbsp; Da die Summe jeder Spalte gleich&nbsp; $1$&nbsp; ist&nbsp; $($das hei&szlig;t: &nbsp; die Summe einer jeden Zeile der transponierten Matrix ergibt ebenfalls $1)$, ist offensichtlich, dass alle Ereigniswahrscheinlichkeiten gleich sein m&uuml;ssen.  
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:$$ {\rm Pr}(A) = {\rm Pr}(B) = {\rm Pr}(C) = 1/3 \hspace{0.15cm}\underline {\approx  0.333}.$$
 +
*The same result is obtained by analyzing the transition matrix.&nbsp; Since the sum of each column equals&nbsp; $1$&nbsp;&nbsp; $($that is,&nbsp; the sum of each row of the transposed matrix also equals $1)$,&nbsp; it is obvious that all event probabilities must be equal.
  
*Auch durch kurzes Nachdenken h&auml;tte man das Ergebnis ohne Rechnung vorhersagen k&ouml;nnen.&nbsp; Da bei jedem Ereignis die Zahlenwerte bei den abgehenden Pfeilen&nbsp; (nur zu anderen Ereignissen)&nbsp; mit denen bei den ankommenden gleich sind, ist nicht einzusehen, warum eines der Ereignisse bevorzugt sein sollte.
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*Even by thinking about it briefly,&nbsp; one could have predicted the result without calculating.&nbsp; Since at each event the numerical values at the outgoing arrows&nbsp; (only to other events)&nbsp; are equal with those at the incoming ones,&nbsp; it is not to be seen why one of the events should be preferred.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
  
[[Category:Theory of Stochastic Signals: Exercises|^1.4 Markovketten
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[[Category:Theory of Stochastic Signals: Exercises|^1.4 Markov Chains
 
^]]
 
^]]

Latest revision as of 14:21, 2 December 2021

Ternary Markov chain

We consider a Markov chain with three possible events  $A$,  $B$,  $C$:

  • The transition probabilities are shown in the graph.
  • Thus,  a transition from  $A$  to  $C$  and vice versa is not possible.
$$p_\text{AC} = p_\text{CA} = 0.$$

The three event probabilities at the starting time  $(\nu = 0)$  are given as follows:

$${\rm Pr}(A_0) = 0,$$
$${\rm Pr}(B_0) = 1,$$
$${\rm Pr}(C_0) = 0.$$



Hints:


Questions

1

Give the transition matrix  ${\mathbf{P}}$  and the transition probabilities  $p_\text{AA}$,  $p_\text{BB}$  and  $p_\text{CC}$.

$p_\text{AA} \ = \ $

$p_\text{BB} \ = \ $

$p_\text{CC} \ = \ $

2

Calculate the event probabilities at time  $\nu = 1$,  in particular

${\rm Pr}(A_1) \ = \ $

3

Calculate the event probabilities at time  $\nu = 2$,  in particular

${\rm Pr}(A_2) \ = \ $

4

What probabilities will occur very long after the Markov chain is turned on  $(ν \rightarrow \infty)$?
In particular, what is the ergodic probability  ${\rm Pr}(A)$?

${\rm Pr}(A) \ = \ $


Solution

(1)  In general  (or in this special case)  it must hold:

$$p_{\rm AA} = 1 - p_{\rm AB} - p_{\rm AC} \hspace{0.5cm} \Rightarrow \hspace{0.5cm} p_{\rm AA} = 1 - 0.75 -0 \hspace{0.15cm}\underline {= 0.25},$$
$$p_{\rm BB} = 1 - p_{\rm BA} - p_{\rm BC} \hspace{0.5cm} \Rightarrow \hspace{0.5cm} p_{\rm BB} = 1 - 0.75 -0.25 \hspace{0.15cm}\underline {= 0},$$
$$p_{\rm CC} = 1 - p_{\rm CA} - p_{\rm CB} \hspace{0.5cm} \Rightarrow \hspace{0.5cm} p_{\rm CC} = 1 - 0 - 0.25 \hspace{0.15cm}\underline {= 0.75}.$$
  • Thus,  the transition matrix is:
$${\mathbf{P}} = \left[ \begin{array}{ccc} 1/4 & 3/4 & 0 \\ 3/4 & 0 & 1/4 \\ 0 & 1/4 & 3/4 \end{array} \right] .$$


(2)  Because of  ${\rm Pr}(B_0) = 1$  and  $p_\text{BB} = 0$,  at time  $\nu = 1$  the event  $B$  cannot occur and  $A$  is much more probable than  $C$:

$$\hspace{0.15cm}\underline {{\rm Pr}(A_1) = 0.75}; \hspace{0.5cm} {\rm Pr}(B_1) = 0; \hspace{0.5cm}{\rm Pr}(C_1) = 0.25.$$
  • The same result is obtained by applying the vector matrix representation.


(3)  For the probability vector at time  $\nu = 2$  holds:

$${\mathbf{p}^{(\nu = 2)}} = {\mathbf{P}}^{\rm T} \cdot {\mathbf{p}^{(\nu =1 )}}= \left[ \begin{array}{ccc} 1/4 & 3/4& 0 \\ 3/4 & 0 & 1/4 \\ 0& 1/4& 3/4 \end{array} \right] \left[ \begin{array}{c} 3/4 \\ 0 \\ 1/4 \end{array} \right] = \left[ \begin{array}{c} 3/16 \\ 10/16 \\ 3/16 \end{array} \right] .$$
  • Thus,  the event probability is  ${\rm Pr}(A_2) = 3/16\hspace{0.15cm}\underline {= 0.1875}$.


(4)  To solve this problem,  several possibilities should be given.

  • One is to solve a system of equations with three unknowns:
$${\rm Pr}(A) = 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(A) \hspace{0.1cm} + \hspace{0.1cm} 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(B),$$
$${\rm Pr}(B) = 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(A) \hspace{2.8cm} + \hspace{0.1cm} 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(C),$$
$${\rm Pr}(C) = \hspace{2.8cm} 1/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(B) \hspace{0.1cm} + \hspace{0.1cm} 3/4 \hspace{0.05cm} \cdot \hspace{0.05cm} {\rm Pr}(C).$$
From the first equation we get  ${\rm Pr}(B) = {\rm Pr}(A)$,  from the last  ${\rm Pr}(C) = {\rm Pr}(A)$.  Since the sum of all probabilities is equal to  $1$,  it follows
$$ {\rm Pr}(A) = {\rm Pr}(B) = {\rm Pr}(C) = 1/3 \hspace{0.15cm}\underline {\approx 0.333}.$$
  • The same result is obtained by analyzing the transition matrix.  Since the sum of each column equals  $1$   $($that is,  the sum of each row of the transposed matrix also equals $1)$,  it is obvious that all event probabilities must be equal.
  • Even by thinking about it briefly,  one could have predicted the result without calculating.  Since at each event the numerical values at the outgoing arrows  (only to other events)  are equal with those at the incoming ones,  it is not to be seen why one of the events should be preferred.