Difference between revisions of "Aufgaben:Exercise 3.1: Cosine-square PDF and PDF with Dirac Functions"

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{{quiz-Header|Buchseite=Stochastische Signaltheorie/Wahrscheinlichkeitsdichtefunktion (WDF)
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{{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Probability_Density_Function_(PDF)
 
}}
 
}}
  
[[File:P_ID143__Sto_A_3_1.png|right|frame|Cosinus–Quadrat–WDF (oben) und Dirac–WDF (unten)]]
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[[File:P_ID143__Sto_A_3_1.png|right|frame|Cosine–Square–PDF (top) and Dirac–PDF (bottom)]]
Die Grafik zeigt die Wahrscheinlichkeitsdichtefunktionen (WDF) zweier Zufallsgrößen  $x$  und  $y$.
+
The graph shows the probability density functions (PDF) of two random variables  $x$  and  $y$.
  
*Die WDF der Zufallsgröße  $x$  lautet in analytischer Form:
+
*The PDF of the random variable  $x$  in analytical form is:
:$$f_x(x)=\left\{\begin{array}{*{4}{c}}A \cdot \cos^2({\pi}/{4}\cdot x)  &\rm f\ddot{u}r\hspace{0.1cm} -2\le \it x\le \rm +2, \\0 & \rm sonst.  \\\end{array}\right.$$
+
:$$f_x(x)=\left\{\begin{array}{*{4}{c}}A \cdot \cos^2({\pi}/{4}\cdot x)  &\rm f\ddot{u}r\hspace{0.1cm} -2\le \it x\le \rm +2, \\0 & \rm else.  \\\end{array}\right.$$
  
*Die WDF der Zufallsgröße  $y$  besteht aus insgesamt fünf Diracfunktionen mit den in der Grafik angegebenen Gewichten.
+
*The PDF of the random variable  $y$  consists of a total of five Dirac functions with the weights given in the graph.
  
  
Betrachtet man diese Zufallsgrößen als Momentanwerte zweier Zufallssignale  $x(t)$  und  $y(t)$, so ist offensichtlich, dass beide Signale auf den Bereich  $\pm 2$  „amplitudenbegrenzt“ sind.  Betragsmäßig größere Werte kommen nicht vor.
+
If we consider these random variables as instantaneous values of two random signals  $x(t)$  and  $y(t)$, it is obvious that both signals are "amplitude limited" to the range  $\pm 2$ . Values larger in absolute value do not occur.
  
  
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''Hinweise:''
+
Hints:
*Die Aufgabe gehört zum  Kapitel  [[Theory_of_Stochastic_Signals/Wahrscheinlichkeitsdichtefunktion|Wahrscheinlichkeitsdichtefunktion]].
+
*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Probability_Density_Function|Probability Density Function]].
*Bezug genommen wird auch auf das Kapitel  [[Theory_of_Stochastic_Signals/Vom_Zufallsexperiment_zur_Zufallsgröße|Vom Zufallsexperiment zur Zufallsgröße]].
+
*Reference is also made to the chapter  [[Theory_of_Stochastic_Signals/From_Random_Experiment_to_Random_Variable|From random experiment to random variable]].
 
   
 
   
*Es gilt folgende Integralgleichung:  
+
*The following integral equation holds:  
:$$\int \cos^{\rm 2}( ax)\, {\rm d}x=\frac{x}{2}+\frac{1}{4 a}\cdot \sin(2 ax).$$
+
:$$\int \cos^{\rm 2}( ax)\, {\rm d}x=\frac{x}{2}+\frac{1}{4 a}\cdot \sin(2 ax).$$
  
  
===Fragebogen===
+
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welche der folgenden Aussagen treffen uneingeschr&auml;nkt zu?
+
{Which of the following statements are absolutely true?
 
|type="[]"}
 
|type="[]"}
+ Die Zufallsgr&ouml;&szlig;e&nbsp; $x$&nbsp; ist wertkontinuierlich.
+
+ The random variable&nbsp; $x$&nbsp; is continuous in value.
+ Die Zufallsgr&ouml;&szlig;e&nbsp; $y$&nbsp; ist wertdiskret.
+
+ The random variable $y$&nbsp; is discrete in value.
- Die Zufallsgr&ouml;&szlig;e&nbsp; $y$&nbsp; ist gleichzeitig zeitdiskret.
+
- The randomness of $y$&nbsp; is also discrete in time.
+ Die WDF sagt nichts aus bezüglich "zeitdiskret/zeitkontinuierlich".
+
+ The PDF says nothing regarding "discrete-time/continuous-time."
  
  
{Berechnen Sie den Parameter&nbsp; $A$&nbsp; der WDF&nbsp; $f_x(x)$.
+
{Calculate the parameter&nbsp; $A$&nbsp; of the PDF&nbsp; $f_x(x)$.
 
|type="{}"}
 
|type="{}"}
$A \ = \ $ { 0.5 3% }
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$A \ = \ $ { 0.5 3% }
  
  
{Wie groß ist die Wahrscheinlichkeit, dass&nbsp; $x = 0$&nbsp; (exakt) gilt?
+
{What is the probability that&nbsp; $x = 0$&nbsp; (exactly) holds?
 
|type="{}"}
 
|type="{}"}
 
${\rm Pr}(x = 0)\ = \ $ { 0. }
 
${\rm Pr}(x = 0)\ = \ $ { 0. }
  
  
{Wie groß ist die Wahrscheinlichkeit, dass&nbsp; $x > 0$&nbsp; ist?
+
{What is the probability that&nbsp; $x > 0$&nbsp;?
 
|type="{}"}
 
|type="{}"}
 
${\rm Pr}(x > 0)\ = \ $ { 0.5 3% }
 
${\rm Pr}(x > 0)\ = \ $ { 0.5 3% }
  
  
{Wie gro&szlig; ist die Wahrscheinlichkeit, dass&nbsp; $y > 0$&nbsp; ist?
+
{What is the probability that&nbsp; $y > 0$&nbsp; is?
 
|type="{}"}
 
|type="{}"}
 
${\rm Pr}(y > 0)\ = \ $ { 0.3 3% }
 
${\rm Pr}(y > 0)\ = \ $ { 0.3 3% }
  
  
{Wie gro&szlig; ist die Wahrscheinlichkeit, dass&nbsp; $y$&nbsp; betragsm&auml;&szlig;ig kleiner als&nbsp; $1$&nbsp; ist?
+
{What is the probability that&nbsp; $y$&nbsp; is smaller than&nbsp; $1$&nbsp; in terms of absolute value?
 
|type="{}"}
 
|type="{}"}
 
${\rm Pr}(|\hspace{0.05cm}y\hspace{0.05cm}| <1)\ = \ $ { 0.4 3% }
 
${\rm Pr}(|\hspace{0.05cm}y\hspace{0.05cm}| <1)\ = \ $ { 0.4 3% }
  
  
{Wie gro&szlig; ist die Wahrscheinlichkeit, dass&nbsp; $x$&nbsp; betragsm&auml;&szlig;ig kleiner als &nbsp; $1$&nbsp; ist?
+
{What is the probability that&nbsp; $x$&nbsp; is smaller than &nbsp; $1$&nbsp; in terms of absolute value?
 
|type="{}"}
 
|type="{}"}
 
${\rm Pr}(|\hspace{0.05cm}x\hspace{0.05cm}| <1)\ = \ $ { 0.818 3% }
 
${\rm Pr}(|\hspace{0.05cm}x\hspace{0.05cm}| <1)\ = \ $ { 0.818 3% }
 
  
  
 
</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
[[File:P_ID174__Sto_A_3_1_b.png|right|frame|Zur Berechnung der WDF-Fläche]]
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[[File:P_ID174__Sto_A_3_1_b.png|right|frame|For calculating PDF area]]
'''(1)'''&nbsp; Richtig sind die <u>Aussagen 1, 2 und 4</u>:
+
'''(1)'''&nbsp; Correct are <u>statements 1, 2, and 4</u>:
* $x$&nbsp;  ist wertkontinuierlich.
+
* $x$&nbsp; is continuous value.
* $y$&nbsp; ist wertdiskret&nbsp; $(M = 5)$.  
+
* $y$&nbsp; is discrete value&nbsp; $(M = 5)$.  
*Die WDF liefert keine Aussagen dar&uuml;ber, ob eine Zufallsgr&ouml;&szlig;e zeitdiskret oder zeitkontinuierlich ist.
+
*The PDF does not provide information about whether a random variable is discrete or continuous in time.
  
  
  
'''(2)'''&nbsp; Die Fl&auml;che unter der WDF muss&nbsp; $1$&nbsp; ergeben.  
+
'''(2)'''&nbsp; The area under the PDF must&nbsp; yield $1$&nbsp;.  
*Durch einfache geometrische &Uuml;berlegungen kommt man zum Ergebnis $\underline{A=0.5}$.
+
*By simple geometric &nbsp; reasoning, one arrives at the result $\underline{A=0.5}$.
  
  
  
'''(3)'''&nbsp; Die Wahrscheinlichkeit, dass die wertkontinuierliche Zufallsgr&ouml;&szlig;e&nbsp; $x$&nbsp; einen festen Wert&nbsp; $x_0$&nbsp; annimmt, ist stets vernachl&auml;ssigbar klein &nbsp; &#8658; &nbsp; $\underline{{\rm Pr}(x = 0) = 0}$.  
+
'''(3)'''&nbsp; The probability that the continuous-valued random variablee&nbsp; $x$&nbsp; takes a fixed value&nbsp; $x_0$&nbsp; is always negligibly small &nbsp; &#8658; &nbsp; $\underline{{\rm Pr}(x = 0) = 0}$.  
*F&uuml;r die wertdiskrete Zufallsgr&ouml;&szlig;e&nbsp; $y$&nbsp; gilt dagegen gemäß der Angabe: &nbsp; ${\rm Pr}(y = 0) = 0.4$&nbsp; $($Gewicht der Diracfunktion bei&nbsp; $y = 0)$.
+
*On the other hand, for the discrete value random variable&nbsp; $y$&nbsp; holds according to the specification: &nbsp; ${\rm Pr}(y = 0) = 0.4$&nbsp; $($weight of the Dirac function at&nbsp; $y = 0)$.
  
  
  
'''(4)'''&nbsp; Wegen&nbsp; ${{\rm Pr}(x = 0) = 0}$&nbsp; und der WDF-Symmetrie ergibt sich&nbsp; $\underline{{\rm Pr}(x > 0) = 0.5}$.
+
'''(4)'''&nbsp; Because of&nbsp; ${{\rm Pr}(x = 0) = 0}$&nbsp; and PDF symmetry, we get&nbsp; $\underline{{\rm Pr}(x > 0) = 0.5}$.
  
  
  
'''(5)'''&nbsp; Da&nbsp; $y$&nbsp; eine diskrete Zufallsgr&ouml;&szlig;e ist, addieren sich die Wahrscheinlichkeiten f&uuml;r&nbsp; $y = 1$&nbsp; und&nbsp; $y = 2$:
+
'''(5)'''&nbsp; Since&nbsp; $y$&nbsp; is a discrete random variable, the probabilities for&nbsp; $y = 1$&nbsp; and&nbsp; $y = 2$ add up:
:$${\rm Pr}(y >0) = {\rm Pr}(y = 1) + {\rm Pr}( y = 2) \hspace{0.15cm}\underline {= 0.3}.$$
+
:$${\rm Pr}(y >0) = {\rm Pr}(y = 1) + {\rm Pr}( y = 2) \hspace{0.15cm}\underline {= 0.3}.$$
  
  
  
'''(6)'''&nbsp; Das Ereignis&nbsp; $|\hspace{0.05cm} y \hspace{0.05cm} | < 1$&nbsp; ist hier identisch mit &nbsp;$y = 0$. Damit erh&auml;lt man:
+
'''(6)'''&nbsp; The event&nbsp; $|\hspace{0.05cm} y \hspace{0.05cm} | < 1$&nbsp; here is identical to &nbsp;$y = 0$. Thus we obtain:
:$${\rm Pr}(|\hspace{0.05cm}y\hspace{0.05cm}| < 1) = {\rm Pr}( y = 0)\hspace{0.15cm}\underline { = 0.4}.$$
+
:$${\rm Pr}(|\hspace{0.05cm}y\hspace{0.05cm}| < 1) = {\rm Pr}( y = 0)\hspace{0.15cm}\underline { = 0.4}.$$
  
  
'''(7)'''&nbsp; Die gesuchte Wahrscheinlichkeit ist gleich dem Integral von&nbsp; $-1$&nbsp; bis&nbsp; $+1$&nbsp; &uuml;ber die WDF der kontinuierlichen Zufallsgr&ouml;&szlig;e&nbsp; $x$.  
+
'''(7)'''&nbsp; The probability we are looking for is equal to the integral from&nbsp; $-1$&nbsp; to&nbsp; $+1$&nbsp; over the PDF of the continuous random variable&nbsp; $x$.  
*Unter Ber&uuml;cksichtigung der Symmetrie und der angegebenen Gleichung erh&auml;lt man:
+
*Taking into account the symmetry and the given equation, we obtain:
 
:$${\rm Pr}(|\hspace{0.05cm} x\hspace{0.05cm}|<1)=2 \cdot \int_{0}^{1}{1}/{2}\cdot \cos^2({\pi}/{4}\cdot x)\hspace{0.1cm}{\rm d}x={x}/{2}+{1}/{\pi}\cdot \sin({\pi}/{2}\cdot x)\Big |_{\rm 0}^{\rm 1}=\rm{1}/{2} + {1}/{\pi}
 
:$${\rm Pr}(|\hspace{0.05cm} x\hspace{0.05cm}|<1)=2 \cdot \int_{0}^{1}{1}/{2}\cdot \cos^2({\pi}/{4}\cdot x)\hspace{0.1cm}{\rm d}x={x}/{2}+{1}/{\pi}\cdot \sin({\pi}/{2}\cdot x)\Big |_{\rm 0}^{\rm 1}=\rm{1}/{2} + {1}/{\pi}
 
\hspace{0.15cm}\underline{
 
\hspace{0.15cm}\underline{
 
\approx 0.818}.$$
 
\approx 0.818}.$$
 
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  

Revision as of 21:40, 24 December 2021

Cosine–Square–PDF (top) and Dirac–PDF (bottom)

The graph shows the probability density functions (PDF) of two random variables  $x$  and  $y$.

  • The PDF of the random variable  $x$  in analytical form is:
$$f_x(x)=\left\{\begin{array}{*{4}{c}}A \cdot \cos^2({\pi}/{4}\cdot x) &\rm f\ddot{u}r\hspace{0.1cm} -2\le \it x\le \rm +2, \\0 & \rm else. \\\end{array}\right.$$
  • The PDF of the random variable  $y$  consists of a total of five Dirac functions with the weights given in the graph.


If we consider these random variables as instantaneous values of two random signals  $x(t)$  and  $y(t)$, it is obvious that both signals are "amplitude limited" to the range  $\pm 2$ . Values larger in absolute value do not occur.





Hints:

  • The following integral equation holds:
$$\int \cos^{\rm 2}( ax)\, {\rm d}x=\frac{x}{2}+\frac{1}{4 a}\cdot \sin(2 ax).$$


Questions

1

Which of the following statements are absolutely true?

The random variable  $x$  is continuous in value.
The random variable $y$  is discrete in value.
The randomness of $y$  is also discrete in time.
The PDF says nothing regarding "discrete-time/continuous-time."

2

Calculate the parameter  $A$  of the PDF  $f_x(x)$.

$A \ = \ $

3

What is the probability that  $x = 0$  (exactly) holds?

${\rm Pr}(x = 0)\ = \ $

4

What is the probability that  $x > 0$ ?

${\rm Pr}(x > 0)\ = \ $

5

What is the probability that  $y > 0$  is?

${\rm Pr}(y > 0)\ = \ $

6

What is the probability that  $y$  is smaller than  $1$  in terms of absolute value?

${\rm Pr}(|\hspace{0.05cm}y\hspace{0.05cm}| <1)\ = \ $

7

What is the probability that  $x$  is smaller than   $1$  in terms of absolute value?

${\rm Pr}(|\hspace{0.05cm}x\hspace{0.05cm}| <1)\ = \ $


Solution

For calculating PDF area

(1)  Correct are statements 1, 2, and 4:

  • $x$  is continuous value.
  • $y$  is discrete value  $(M = 5)$.
  • The PDF does not provide information about whether a random variable is discrete or continuous in time.


(2)  The area under the PDF must  yield $1$ .

  • By simple geometric   reasoning, one arrives at the result $\underline{A=0.5}$.


(3)  The probability that the continuous-valued random variablee  $x$  takes a fixed value  $x_0$  is always negligibly small   ⇒   $\underline{{\rm Pr}(x = 0) = 0}$.

  • On the other hand, for the discrete value random variable  $y$  holds according to the specification:   ${\rm Pr}(y = 0) = 0.4$  $($weight of the Dirac function at  $y = 0)$.


(4)  Because of  ${{\rm Pr}(x = 0) = 0}$  and PDF symmetry, we get  $\underline{{\rm Pr}(x > 0) = 0.5}$.


(5)  Since  $y$  is a discrete random variable, the probabilities for  $y = 1$  and  $y = 2$ add up:

$${\rm Pr}(y >0) = {\rm Pr}(y = 1) + {\rm Pr}( y = 2) \hspace{0.15cm}\underline {= 0.3}.$$


(6)  The event  $|\hspace{0.05cm} y \hspace{0.05cm} | < 1$  here is identical to  $y = 0$. Thus we obtain:

$${\rm Pr}(|\hspace{0.05cm}y\hspace{0.05cm}| < 1) = {\rm Pr}( y = 0)\hspace{0.15cm}\underline { = 0.4}.$$


(7)  The probability we are looking for is equal to the integral from  $-1$  to  $+1$  over the PDF of the continuous random variable  $x$.

  • Taking into account the symmetry and the given equation, we obtain:
$${\rm Pr}(|\hspace{0.05cm} x\hspace{0.05cm}|<1)=2 \cdot \int_{0}^{1}{1}/{2}\cdot \cos^2({\pi}/{4}\cdot x)\hspace{0.1cm}{\rm d}x={x}/{2}+{1}/{\pi}\cdot \sin({\pi}/{2}\cdot x)\Big |_{\rm 0}^{\rm 1}=\rm{1}/{2} + {1}/{\pi} \hspace{0.15cm}\underline{ \approx 0.818}.$$