Difference between revisions of "Aufgaben:Exercise 3.1Z: Drawing Cards"

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{{quiz-Header|Buchseite=Informationstheorie/Einige Vorbemerkungen zu zweidimensionalen Zufallsgrößen
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{{quiz-Header|Buchseite=Information_Theory/Some_Preliminary_Remarks_on_Two-Dimensional_Random_Variables
 
}}
 
}}
  
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In contrast, for the other sub-questions from&nbsp; '''(2)'''&nbsp; onwards, you should assume that the three cards are drawn all at once&nbsp; <br>("draw without putting back").
+
In contrast, for the other sub-questions from&nbsp; '''(2)'''&nbsp; onwards, you should assume that the three cards are drawn all at once&nbsp; <br>("card draw without putting back").
  
 
*In the following, we use&nbsp; $A_i$&nbsp; to denote the event that the card drawn at time&nbsp; $i$&nbsp; is an ace. &nbsp; <br>Here we have to set&nbsp; $i = 1,\ 2,\ 3$&nbsp;.  
 
*In the following, we use&nbsp; $A_i$&nbsp; to denote the event that the card drawn at time&nbsp; $i$&nbsp; is an ace. &nbsp; <br>Here we have to set&nbsp; $i = 1,\ 2,\ 3$&nbsp;.  
*The complementary event then states that at time&nbsp; $i$&nbsp; no ace is drawn, but any other card.
+
*The complementary event&nbsp; $\overline{\it A_i}$&nbsp; then states that at time&nbsp; $i$&nbsp; no ace is drawn, but any other card.
  
  
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*The exercise belongs to the chapter&nbsp; [[Information_Theory/Einige_Vorbemerkungen_zu_zweidimensionalen_Zufallsgrößen|Some preliminary remarks on 2D random variables]].
 
*The exercise belongs to the chapter&nbsp; [[Information_Theory/Einige_Vorbemerkungen_zu_zweidimensionalen_Zufallsgrößen|Some preliminary remarks on 2D random variables]].
 
*In particular, the subject matter of the chapter&nbsp;  [[Theory_of_Stochastic_Signals/Statistische_Abhängigkeit_und_Unabhängigkeit|Statistical Dependence and Independence]] in the book "Stochastic Signal Theory" is repeated here.
 
*In particular, the subject matter of the chapter&nbsp;  [[Theory_of_Stochastic_Signals/Statistische_Abhängigkeit_und_Unabhängigkeit|Statistical Dependence and Independence]] in the book "Stochastic Signal Theory" is repeated here.
*A summary of the theoretical basics with examples can be found in the (German language) learning video&nbsp;[[Statistische_Abhängigkeit_und_Unabhängigkeit_(Lernvideo)|Statistische Abhängigkeit und Unabhängigkeit ]]&nbsp; &rArr; &nbsp;  "Statistical Dependence and Independence".  
+
*A summary of the theoretical basics with examples can be found in the (German language) learning video&nbsp;<br> &nbsp; &nbsp; &nbsp; [[Statistische_Abhängigkeit_und_Unabhängigkeit_(Lernvideo)|Statistische Abhängigkeit und Unabhängigkeit ]]&nbsp; &rArr; &nbsp;  "Statistical Dependence and Independence".  
 
   
 
   
  
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First, consider the case of „sampling with replacement“.&nbsp; What is the probability&nbsp; $p_1$, that three aces will be drawn?
+
First, consider the case of&nbsp; "card draw with putting back".&nbsp; What is the probability&nbsp; $p_1$, that three aces will be drawn?
 
|type="{}"}
 
|type="{}"}
 
  $p_1 \ = \ $  { 0.002 3%  }
 
  $p_1 \ = \ $  { 0.002 3%  }
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$p_2 \ = \ $ { 0.0008 3% }
 
$p_2 \ = \ $ { 0.0008 3% }
  
{Consider further the case of „sampling without replacement“.&nbsp; What is the probability&nbsp; $p_3$ that not a single ace is drawn?
+
{Consider further the case of&nbsp; "card draw without putting back".&nbsp; What is the probability&nbsp; $p_3$ that not a single ace is drawn?
 
|type="{}"}
 
|type="{}"}
 
$p_3 \ = \ $ { 0.6605 3% }
 
$p_3 \ = \ $ { 0.6605 3% }
  
{What is the probability&nbsp; $p_4$ that exactly one ace is drawn in the case „sampling without replacement“?
+
{What is the probability&nbsp; $p_4$ that exactly one ace is drawn in the case&nbsp; "card draw without putting back"?
 
|type="{}"}
 
|type="{}"}
 
$p_4 \ = \ $ { 0.3048 3% }  
 
$p_4 \ = \ $ { 0.3048 3% }  
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'''(1)'''&nbsp; If the cards are put back after being drawn, the probability of an ace is the same at every time&nbsp; $(1/8)$:
 
'''(1)'''&nbsp; If the cards are put back after being drawn, the probability of an ace is the same at every time&nbsp; $(1/8)$:
 
    
 
    
:$$ p_{\rm 1} = \rm Pr (3 \hspace{0.1cm} Asse) = \rm Pr (\it A_{\rm 1} \rm )\cdot \rm Pr (\it A_{\rm 2} \rm )\cdot \rm Pr (\it A_{\rm 3} \rm ) = \rm \big({1}/{8}\big)^3 \hspace{0.15cm}\underline{\approx 0.002}.$$
+
:$$ p_{\rm 1} = \rm Pr (3 \hspace{0.1cm} aces) = \rm Pr (\it A_{\rm 1} \rm )\cdot \rm Pr (\it A_{\rm 2} \rm )\cdot \rm Pr (\it A_{\rm 3} \rm ) = \rm \big({1}/{8}\big)^3 \hspace{0.15cm}\underline{\approx 0.002}.$$
  
 
   
 
   
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'''(3)'''&nbsp; Analogous to sub-task&nbsp; '''(2)'''&nbsp;, we obtain here:
+
'''(3)'''&nbsp; Analogous to sub-task&nbsp; '''(2)''',&nbsp; we obtain here:
  
 
:$$p_{\rm 3} = \rm Pr (\overline{\it A_{\rm 1}})\cdot \rm Pr (\overline{\it A_{\rm 2}} \hspace{0.05cm}|\hspace{0.05cm}\overline{\it A_{\rm 1}})\cdot \rm Pr (\overline{\it A_{\rm3}}\hspace{0.05cm}|\hspace{0.05cm}(\overline{\it A_{\rm 1}} \cap \overline{\it A_{\rm 2}} )) =\rm \frac{28}{32}\cdot\frac{27}{31}\cdot\frac{26}{30}\hspace{0.15cm}\underline{\approx 0.6605}.$$
 
:$$p_{\rm 3} = \rm Pr (\overline{\it A_{\rm 1}})\cdot \rm Pr (\overline{\it A_{\rm 2}} \hspace{0.05cm}|\hspace{0.05cm}\overline{\it A_{\rm 1}})\cdot \rm Pr (\overline{\it A_{\rm3}}\hspace{0.05cm}|\hspace{0.05cm}(\overline{\it A_{\rm 1}} \cap \overline{\it A_{\rm 2}} )) =\rm \frac{28}{32}\cdot\frac{27}{31}\cdot\frac{26}{30}\hspace{0.15cm}\underline{\approx 0.6605}.$$
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'''(4)'''&nbsp; This probability can be expressed as the sum of three probabilities. &nbsp; &rArr; &nbsp; $p_{\rm 4} = \rm Pr (\it D_{\rm 1} \cup \it D_{\rm 2} \cup \it D_{\rm 3}) $.
+
'''(4)'''&nbsp; This probability can be expressed as the sum of three probabilities  &nbsp; &rArr; &nbsp; $p_{\rm 4} = \rm Pr (\it D_{\rm 1} \cup \it D_{\rm 2} \cup \it D_{\rm 3}) $.
* The corresponding events&nbsp; ${\rm Pr}(D_1)$,&nbsp;  ${\rm Pr}(D_2)$&nbsp; and&nbsp; ${\rm Pr}(D_3)$&nbsp; are disjoint:
+
* The corresponding events&nbsp; $D_1$,&nbsp;  $D_2$&nbsp; and&nbsp; $D_3$&nbsp; are disjoint:
  
:$$\rm Pr (\it D_{\rm 1}) = \rm Pr (\it A_{\rm 1} \cap \overline{ \it A_{\rm 2}} \cap \overline{\it A_{\rm 3}}) = \rm \frac{4}{32}\cdot \frac{28}{31}\cdot \frac{27}{30}=\rm 0.1016,$$
+
:$${\rm Pr} (D_1) = {\rm Pr} (A_1 \cap \overline{ \it A_{\rm 2}} \cap \overline{\it A_{\rm 3}}) = \rm \frac{4}{32}\cdot \frac{28}{31}\cdot \frac{27}{30}=\rm 0.1016,$$
:$$\rm Pr (\it D_{\rm 2}) =  \rm Pr ( \overline{\it A_{\rm 1}} \cap \it A_{\rm 2} \cap \overline{\it A_{\rm 3}})  = \rm \frac{28}{32}\cdot \frac{4}{31}\cdot\frac{27}{30}=\rm 0.1016,$$
+
:$${\rm Pr} (D_2) =  {\rm Pr} ( \overline{A_1} \cap A_2 \cap \overline{A_3})  = \rm \frac{28}{32}\cdot \frac{4}{31}\cdot\frac{27}{30}=\rm 0.1016,$$
:$$\rm Pr (\it D_{\rm 3}) =  \rm Pr ( \overline{\it A_{\rm 1}} \cap  \overline{\it A_{\rm 2}} \cap \it A_{\rm 3}) = \rm \frac{28}{32}\cdot \frac{27}{31}\cdot \frac{4}{30}=\rm 0.1016.$$
+
:$${\rm Pr} (D_3) =  {\rm Pr} ( \overline{A_1} \cap  \overline{A_2} \cap A_3) = \rm \frac{28}{32}\cdot \frac{27}{31}\cdot \frac{4}{30}=\rm 0.1016.$$
  
 
*These probabilities are all the same - why should it be any different?
 
*These probabilities are all the same - why should it be any different?
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*This gives us for the sum:
 
*This gives us for the sum:
  
:$$p_{\rm 4}= \rm Pr (\it D_{\rm 1} \cup \it D_{\rm 2} \cup \it D_{\rm 3}) \rm \hspace{0.15cm}\underline{= 0.3084}.$$
+
:$$p_{\rm 4}= {\rm Pr} (D_1 \cup D_2 \cup D_3) \rm \hspace{0.15cm}\underline{= 0.3084}.$$
  
  
'''(5)'''&nbsp; If one defines the events&nbsp; $E_i =$&nbsp; "Exactly&nbsp; $i$&nbsp; aces are drawn" with the indices&nbsp; $i = 0,\ 1,\ 2,\ 3$,  
+
'''(5)'''&nbsp; If one defines the events&nbsp; $E_i =$&nbsp; &raquo;Exactly&nbsp; $i$&nbsp; aces are drawn&laquo;&nbsp; with the indices&nbsp; $i = 0,\ 1,\ 2,\ 3$,  
*then&nbsp; $E_0$,&nbsp; $E_1$,&nbsp; $E_2$&nbsp; and $E_3$&nbsp; describe a complete system.
+
*then&nbsp; $E_0$,&nbsp; $E_1$,&nbsp; $E_2$&nbsp; and $E_3$&nbsp; describe a&nbsp; "complete system".
 
*Therefore:
 
*Therefore:
:$$p_{\rm 5} = \rm Pr (\it E_{\rm 2}) = \rm 1 - \it p_{\rm 2} -\it p_{\rm 3} - \it p_{\rm 4} \hspace{0.15cm}\underline{= \rm 0.0339}.$$
+
:$$p_{\rm 5} = {\rm Pr} (E_2) = 1 - p_{\rm 2} - p_{\rm 3} - p_{\rm 4} \hspace{0.15cm}\underline{= \rm 0.0339}.$$
  
 
{{ML-Fuß}}
 
{{ML-Fuß}}

Latest revision as of 10:09, 24 September 2021

The desired result
»Three aces are drawn«

From a deck of  $32$  cards, including four aces, three cards are drawn in succession.  For question  (1),  it is assumed that after a card has been drawn

  • it is put back into the deck,
  • the deck is reshuffled and
  • then the next card is drawn.


In contrast, for the other sub-questions from  (2)  onwards, you should assume that the three cards are drawn all at once 
("card draw without putting back").

  • In the following, we use  $A_i$  to denote the event that the card drawn at time  $i$  is an ace.  
    Here we have to set  $i = 1,\ 2,\ 3$ .
  • The complementary event  $\overline{\it A_i}$  then states that at time  $i$  no ace is drawn, but any other card.




Hints:



Questions

1

First, consider the case of  "card draw with putting back".  What is the probability  $p_1$, that three aces will be drawn?

$p_1 \ = \ $

2

What is the probability  $p_2$  that three aces will be drawn if the cards are not put back?  Why is  $p_2$  smaller/equal/larger than  $p_1$?

$p_2 \ = \ $

3

Consider further the case of  "card draw without putting back".  What is the probability  $p_3$ that not a single ace is drawn?

$p_3 \ = \ $

4

What is the probability  $p_4$ that exactly one ace is drawn in the case  "card draw without putting back"?

$p_4 \ = \ $

5

What is the probability that two of the three drawn cards are aces?
Note:   The events „exactly  $i$  aces are drawn” with  $i = 0,\ 1,\ 2,\ 3$  describe a so-called  "complete system".

$p_5 \ = \ $


Solution

(1)  If the cards are put back after being drawn, the probability of an ace is the same at every time  $(1/8)$:

$$ p_{\rm 1} = \rm Pr (3 \hspace{0.1cm} aces) = \rm Pr (\it A_{\rm 1} \rm )\cdot \rm Pr (\it A_{\rm 2} \rm )\cdot \rm Pr (\it A_{\rm 3} \rm ) = \rm \big({1}/{8}\big)^3 \hspace{0.15cm}\underline{\approx 0.002}.$$


(2)  Now, using the general multiplication theorem, we obtain:

$$ p_{\rm 2} = \rm Pr (\it A_{\rm 1}\cap \it A_{\rm 2} \cap \it A_{\rm 3} \rm ) = \rm Pr (\it A_{\rm 1}\rm ) \cdot \rm Pr (\it A_{\rm 2} |\it A_{\rm 1}\rm ) \cdot \rm Pr (\it A_{\rm 3} |( \it A_{\rm 1}\cap \it A_{\rm 2} \rm )).$$
  • The conditional probabilities can be calculated according to the classical definition.
  • One thus obtains the result  $k/m$  $($with  $m$  cards there are still  $k$  aces$)$:
$$p_{\rm 2} =\rm \frac{4}{32}\cdot \frac{3}{31}\cdot\frac{2}{30}\hspace{0.15cm}\underline{ \approx 0.0008}.$$
  • $p_2$  is smaller than  $p_1$, because now the second and third aces are less likely than before.


(3)  Analogous to sub-task  (2),  we obtain here:

$$p_{\rm 3} = \rm Pr (\overline{\it A_{\rm 1}})\cdot \rm Pr (\overline{\it A_{\rm 2}} \hspace{0.05cm}|\hspace{0.05cm}\overline{\it A_{\rm 1}})\cdot \rm Pr (\overline{\it A_{\rm3}}\hspace{0.05cm}|\hspace{0.05cm}(\overline{\it A_{\rm 1}} \cap \overline{\it A_{\rm 2}} )) =\rm \frac{28}{32}\cdot\frac{27}{31}\cdot\frac{26}{30}\hspace{0.15cm}\underline{\approx 0.6605}.$$


(4)  This probability can be expressed as the sum of three probabilities   ⇒   $p_{\rm 4} = \rm Pr (\it D_{\rm 1} \cup \it D_{\rm 2} \cup \it D_{\rm 3}) $.

  • The corresponding events  $D_1$,  $D_2$  and  $D_3$  are disjoint:
$${\rm Pr} (D_1) = {\rm Pr} (A_1 \cap \overline{ \it A_{\rm 2}} \cap \overline{\it A_{\rm 3}}) = \rm \frac{4}{32}\cdot \frac{28}{31}\cdot \frac{27}{30}=\rm 0.1016,$$
$${\rm Pr} (D_2) = {\rm Pr} ( \overline{A_1} \cap A_2 \cap \overline{A_3}) = \rm \frac{28}{32}\cdot \frac{4}{31}\cdot\frac{27}{30}=\rm 0.1016,$$
$${\rm Pr} (D_3) = {\rm Pr} ( \overline{A_1} \cap \overline{A_2} \cap A_3) = \rm \frac{28}{32}\cdot \frac{27}{31}\cdot \frac{4}{30}=\rm 0.1016.$$
  • These probabilities are all the same - why should it be any different?
  • If you draw exactly one ace from three cards, it is just as likely whether you draw this as the first, second or third card.
  • This gives us for the sum:
$$p_{\rm 4}= {\rm Pr} (D_1 \cup D_2 \cup D_3) \rm \hspace{0.15cm}\underline{= 0.3084}.$$


(5)  If one defines the events  $E_i =$  »Exactly  $i$  aces are drawn«  with the indices  $i = 0,\ 1,\ 2,\ 3$,

  • then  $E_0$,  $E_1$,  $E_2$  and $E_3$  describe a  "complete system".
  • Therefore:
$$p_{\rm 5} = {\rm Pr} (E_2) = 1 - p_{\rm 2} - p_{\rm 3} - p_{\rm 4} \hspace{0.15cm}\underline{= \rm 0.0339}.$$