Difference between revisions of "Aufgaben:Exercise 3.9: Characteristic Curve for Cosine PDF"

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[[File:P_ID136__Sto_A_3_9.png|right|frame|Rechteck– und Cosinus–WDF]]
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[[File:P_ID136__Sto_A_3_9.png|right|frame|Rectangular and cosine PDF]]
Gesucht ist eine stetige, monoton steigende nichtlineare Kennlinie  $y =g(x)$, die aus einer zwischen  $-1$  und  $+1$  gleichverteilten Zufallsgröße  $x$  eine neue Zufallsgröße  $y$  mit „cosinusförmiger” WDF generiert:
+
We are looking for a continuous,  monotonically increasing nonlinear characteristic  $y =g(x)$,  which generates a new random variable  with  "cosine"  PDF from a between  $-1$  and  $+1$  uniformly distributed random variable  $x$:
 
:$$f_y(y)=A\cdot\cos({\pi}/{2}\cdot y).$$
 
:$$f_y(y)=A\cdot\cos({\pi}/{2}\cdot y).$$
  
*Die Zufallsgröße  $y$  kann ebenfalls nur Werte zwischen  $-1$  und  $+1$  annehmen.  
+
*The random variable  $y$  can also only take values between  $-1$  and  $+1$.  
*Die beiden  Dichtefunktionen  $f_x(x)$  und  $f_y(y)$  sind nebenstehend skizziert.
+
*The two density functions  $f_x(x)$  and  $f_y(y)$  are sketched on the right.
  
  
  
  
 
+
Hints:  
 
+
*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Exponentially_Distributed_Random_Variables|Exponentially Distributed Random Variables]].
 
+
*In particular, reference is made to the page  [[Theory_of_Stochastic_Signals/Exponentially_Distributed_Random_Variables#Transformation_of_random_variables|Transformation of random variables]].
 
 
''Hinweise:''
 
*Die Aufgabe gehört zum  Kapitel  [[Stochastische_Signaltheorie/Exponentialverteilte_Zufallsgrößen|Exponentialverteilte Zufallsgrößen]].
 
*Insbesondere wird Bezug genommen auf die Seite  [[Stochastische_Signaltheorie/Exponentialverteilte_Zufallsgrößen#Transformation_von_Zufallsgr.C3.B6.C3.9Fen|Transformation von Zufallsgrößen]].
 
 
   
 
   
  
  
  
===Fragebogen===
+
===Question===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welche der folgenden Aussagen sind zutreffend?
+
{Which of the following statements are true?
 
|type="[]"}
 
|type="[]"}
+ Au&szlig;erhalb des Bereichs&nbsp; $-1 \le x \le +1$&nbsp; kann&nbsp; $g(x)$&nbsp; beliebig sein.
+
+ Outside the range&nbsp; $-1 \le x \le +1$&nbsp; &rArr; &nbsp; $g(x)$&nbsp; can be arbitrary.
- Die Kennlinie muss symmetrisch um&nbsp; $x= 0$&nbsp; sein: &nbsp; $g(-x) = g(x)$.
+
- The characteristic curve must be symmetrical about&nbsp; $x= 0$&nbsp; : &nbsp; $g(-x) = g(x)$.
+ Die Zufallsgr&ouml;&szlig;e&nbsp; $y$&nbsp; hat eine kleinere Varianz als&nbsp; $x$.
+
+ The random variable&nbsp; $y$&nbsp; has a smaller variance than&nbsp; $x$.
  
  
{Berechnen Sie den&nbsp; $f_y(y)$&ndash;Wert bei&nbsp; $y = 0$: &nbsp; $A = f_y(0)$.
+
{Calculate the&nbsp; $f_y(y)$ value at&nbsp; $y = 0$: &nbsp; $A = f_y(0)$.
 
|type="{}"}
 
|type="{}"}
$A \ = \ $ { 0.785 3% }
+
$A \ = \ $ { 0.785 3% }
  
  
{Bestimmen Sie die Steigung&nbsp; $h\hspace{0.05cm}'(y)$&nbsp; der Umkehrfunktion&nbsp; $x = h(y)$, wobei für&nbsp; $|y| \le 1$&nbsp; stets&nbsp; $h\hspace{0.05cm}'(y) > 0$&nbsp; gelten soll?&nbsp; Welche Steigung gilt bei&nbsp; $y = 0$&nbsp;?
+
{Determine the slope&nbsp; $h\hspace{0.05cm}'(y)$&nbsp; of the inverse function&nbsp; $x = h(y)$,&nbsp; where for&nbsp; $|y| \le 1$&nbsp; always&nbsp; $h\hspace{0.05cm}'(y) > 0$&nbsp; should hold?&nbsp; What slope holds for&nbsp; $y = 0$&nbsp;?
 
|type="{}"}
 
|type="{}"}
 
$h'(y = 0) \ = \ $ { 1.571 3% }
 
$h'(y = 0) \ = \ $ { 1.571 3% }
  
  
{Berechnen Sie mit dem Ergebnis aus&nbsp; '''(3)'''&nbsp; die Funktion&nbsp; $x = h(y)$&nbsp; unter der Nebenbedingung&nbsp; $h(0) = 0$.&nbsp; Welcher Wert ergibt sich f&uuml;r&nbsp; $y = 1$&nbsp;?
+
{Compute with the result from&nbsp; '''(3)'''&nbsp; the function&nbsp; $x = h(y)$&nbsp; under the constraint&nbsp; $h(0) = 0$.&nbsp; What value results f&uuml;r&nbsp; $y = 1$&nbsp;?
 
|type="{}"}
 
|type="{}"}
 
$h(y=1) \ = \ $ { 1 3% }
 
$h(y=1) \ = \ $ { 1 3% }
  
  
{Ermitteln Sie den Funktionsverlauf&nbsp; $y = g(x)$&nbsp; der gesuchten Kennlinie.&nbsp; Welcher Funktionswert ergibt sich an der Stelle&nbsp; $x = 1$&nbsp;?
+
{Determine the function&nbsp; $y = g(x)$&nbsp; of the characteristic we are looking for.&nbsp; What is the function value at the point&nbsp; $x = 1$&nbsp;?
 
|type="{}"}
 
|type="{}"}
 
$g(x = 1) \ = \ $ { 1 3% }
 
$g(x = 1) \ = \ $ { 1 3% }
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</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Richtig sind <u>die Aussagen 1 und 3</u>:
 
*Da&nbsp; $x$&nbsp; nur Werte zwischen&nbsp; $\pm 1$&nbsp; annehmen kann, ist der Verlauf der Kennlinie au&szlig;erhalb dieses Bereichs f&uuml;r die Zufallsgr&ouml;&szlig;e&nbsp; $y$&nbsp; ohne Belang.
 
*Die Bedingung&nbsp; $g(-x) = g(x)$&nbsp; muss nicht eingehalten werden.&nbsp; Es gibt beliebig viele Kennlinien, die die gew&uuml;nschte WDF erzeugen k&ouml;nnen.
 
*Die unter Punkt&nbsp; '''(5)'''&nbsp; berechnete Kennlinie ist beispielsweise punktsymmetrisch: &nbsp; $g(-x) = -g(x)$.
 
*Schon die grafischen Darstellungen der beiden Dichtefunktionen zeigen, dass&nbsp; $\sigma_y^2 < \sigma_x^2$&nbsp;  ist.
 
  
 +
'''(1)'''&nbsp; Correct are the&nbsp; <u>statements 1 and 3</u>:
 +
*Since&nbsp; $x$&nbsp; can only take values between&nbsp; $\pm 1$,&nbsp; the course of the characteristic curve outside of this range is irrelevant for the random variable&nbsp; $y$.
 +
*The condition&nbsp; $g(-x) = g(x)$&nbsp; does not have to be met.&nbsp; There are any number of characteristic curves that can generate the desired PDF.
 +
*For example,&nbsp; the characteristic curve calculated in point&nbsp; '''(5)'''&nbsp; is point-symmetric: &nbsp; $g(-x) = -g(x)$.
 +
*The graphical representations of the two density functions already show that&nbsp; $\sigma_y^2 < \sigma_x^2$.
  
  
'''(2)'''&nbsp; Das Integral &uuml;ber die WDF muss stets gleich&nbsp; $1$&nbsp; sein. Daraus folgt:
 
:$$\int_{-\rm 1}^{\rm 1}A\cdot \cos({\pi}/{\rm 2}\cdot y)\, {\rm d} y=\frac{A\cdot \rm 4}{\pi}\hspace{0.3cm} \Rightarrow\hspace{0.3cm} A=\frac{\pi}{\rm 4} \hspace{0.15cm}\underline{= \rm 0.785}.$$
 
  
 +
'''(2)'''&nbsp; The integral over the PDF must always equal&nbsp; $1$&nbsp;. It follows that:
 +
:$$\int_{-\rm 1}^{\rm 1}A\cdot \cos({\pi}/{\rm 2}\cdot y)\, {\rm d} y=\frac{A\cdot \rm 4}{\pi}\hspace{0.3cm} \rightarrow\hspace{0.3cm} A=\frac{\pi}{\rm 4} \hspace{0.15cm}\underline{= \rm 0.785}.$$
  
  
'''(3)'''&nbsp; Die Transformationsformel kann wie folgt umgeformt werden:
+
 
 +
'''(3)'''&nbsp; The transformation formula can be described as follows:
 
:$$f_y(y)=\frac{f_x(x)}{| g'(x)|}\Big|_{\, x=h(y)}=f_x(x)\cdot |h'(y)| \Big|_{\, x=h(y)}.$$
 
:$$f_y(y)=\frac{f_x(x)}{| g'(x)|}\Big|_{\, x=h(y)}=f_x(x)\cdot |h'(y)| \Big|_{\, x=h(y)}.$$
  
*Die Umkehrfunktion&nbsp; $x = h(y)$&nbsp; einer monoton ansteigenden Kennlinie&nbsp; $y = g(x)$&nbsp; steigt ebenfalls monoton an.
+
*The inverse function&nbsp; $x = h(y)$&nbsp; of a monotonically increasing characteristic&nbsp; $y = g(x)$&nbsp; also increases monotonically.
*Deshalb kann auf die Betragsbildung verzichtet werden und man erh&auml;lt:
+
*Therefore one does not need to make use of the absolute value and subsequently obtains:
:$$h\hspace{0.05cm}'(y)=\frac{f_y(y)}{f_x(x)\Big|_{\, x=h(y)}}={\pi}/{\rm 2}\cdot \cos({\pi}/{2}\cdot y).$$
+
:$$h\hspace{0.05cm}'(y)=\frac{f_y(y)}{f_x(x)\Big|_{\, x=h(y)}}={\pi}/{\rm 2}\cdot \cos({\pi}/{2}\cdot y).$$
 
 
*An der Stelle&nbsp; $y = 0$&nbsp; hat die Steigung den Wert&nbsp; $h\hspace{0.05cm}'(y= 0)=&pi;/2\hspace{0.15cm}\underline{\approx 1.571}$.
 
  
 +
*At the point&nbsp; $y = 0$&nbsp; the slope has the value&nbsp; $h\hspace{0.05cm}'(y= 0)=&pi;/2\hspace{0.15cm}\underline{\approx 1.571}$.
  
  
'''(4)'''&nbsp; Man erh&auml;lt durch (unbestimmte) Integration:
 
:$$h(y)=\int h\hspace{0.05cm}'(y)\, {\rm d} y + C = \frac{\pi}{2}\cdot \frac{2}{\pi}\cdot \sin(\frac{\pi}{ 2}\cdot  y) +  C.$$
 
  
*Die Nebenbedingung&nbsp; $h(y= 0) = 0$&nbsp; f&uuml;hrt zur Konstanten&nbsp; $C = 0$&nbsp; und damit zum Ergebnis:
+
'''(4)'''&nbsp; One obtains by (indefinite) integration:
:$$h(y) = \sin({\pi}/{2}\cdot y) \hspace{0.5cm} \Rightarrow\hspace{0.5cm}
+
:$$h(y)=\int h\hspace{0.05cm}'(y)\, {\rm d} y + C = \frac{\pi}{2}\cdot \frac{2}{\pi}\cdot \sin(\frac{\pi}{ 2}\cdot y) + C.$$
h(y = 1)  \hspace{0.15cm}\underline{= +1}.$$
 
  
 +
*The constraint&nbsp; $h(y= 0) = 0$&nbsp; leads to the constant&nbsp; $C = 0$&nbsp; and thus to the result:
 +
:$$h(y) = \sin({\pi}/{2}\cdot y) \hspace{0.5cm} \rightarrow\hspace{0.5cm}
 +
h(y = 1) \hspace{0.15cm}\underline{= +1}.$$
  
  
'''(5)'''&nbsp; Die Umkehrfunktion der in der Teilaufgabe&nbsp; '''(4)'''&nbsp; ermittelten Funktion&nbsp; $x = h(y)$&nbsp; lautet:
+
'''(5)'''&nbsp; The inverse function of the function determined in subtask&nbsp; '''(4)'''&nbsp; is&nbsp; $x = h(y)$&nbsp; :
 
:$$y=g(x)={\rm 2}/{\rm \pi}\cdot \rm arcsin({\it x}).$$
 
:$$y=g(x)={\rm 2}/{\rm \pi}\cdot \rm arcsin({\it x}).$$
  
*Diese Kennlinie steigt im Bereich&nbsp; $-1 \le x \le +1$&nbsp; von &nbsp;$y = -1$&nbsp; bis &nbsp;$y = +1$&nbsp; monoton an.  
+
*This characteristic curve increases monotonically in the range&nbsp; $-1 \le x \le +1$&nbsp; from &nbsp;$y = -1$&nbsp; to &nbsp;$y = +1$&nbsp;.  
*Der gesuchte Wert ist also&nbsp; $g(x= 1) \hspace{0.15cm}\underline{= +1}$.
+
*So the value we are looking for is&nbsp; $g(x= 1) \hspace{0.15cm}\underline{= +1}$.
  
 
{{ML-Fuß}}
 
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[[Category:Aufgaben zu Stochastische Signaltheorie|^3.6 Exponentialverteilte Zufallsgrößen^]]
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[[Category:Theory of Stochastic Signals: Exercises|^3.6 Exponentially Distributed Random Variables^]]

Latest revision as of 15:27, 2 February 2022

Rectangular and cosine PDF

We are looking for a continuous,  monotonically increasing nonlinear characteristic  $y =g(x)$,  which generates a new random variable  with  "cosine"  PDF from a between  $-1$  and  $+1$  uniformly distributed random variable  $x$:

$$f_y(y)=A\cdot\cos({\pi}/{2}\cdot y).$$
  • The random variable  $y$  can also only take values between  $-1$  and  $+1$.
  • The two density functions  $f_x(x)$  and  $f_y(y)$  are sketched on the right.



Hints:



Question

1

Which of the following statements are true?

Outside the range  $-1 \le x \le +1$  ⇒   $g(x)$  can be arbitrary.
The characteristic curve must be symmetrical about  $x= 0$  :   $g(-x) = g(x)$.
The random variable  $y$  has a smaller variance than  $x$.

2

Calculate the  $f_y(y)$ value at  $y = 0$:   $A = f_y(0)$.

$A \ = \ $

3

Determine the slope  $h\hspace{0.05cm}'(y)$  of the inverse function  $x = h(y)$,  where for  $|y| \le 1$  always  $h\hspace{0.05cm}'(y) > 0$  should hold?  What slope holds for  $y = 0$ ?

$h'(y = 0) \ = \ $

4

Compute with the result from  (3)  the function  $x = h(y)$  under the constraint  $h(0) = 0$.  What value results für  $y = 1$ ?

$h(y=1) \ = \ $

5

Determine the function  $y = g(x)$  of the characteristic we are looking for.  What is the function value at the point  $x = 1$ ?

$g(x = 1) \ = \ $


Solution

(1)  Correct are the  statements 1 and 3:

  • Since  $x$  can only take values between  $\pm 1$,  the course of the characteristic curve outside of this range is irrelevant for the random variable  $y$.
  • The condition  $g(-x) = g(x)$  does not have to be met.  There are any number of characteristic curves that can generate the desired PDF.
  • For example,  the characteristic curve calculated in point  (5)  is point-symmetric:   $g(-x) = -g(x)$.
  • The graphical representations of the two density functions already show that  $\sigma_y^2 < \sigma_x^2$.


(2)  The integral over the PDF must always equal  $1$ . It follows that:

$$\int_{-\rm 1}^{\rm 1}A\cdot \cos({\pi}/{\rm 2}\cdot y)\, {\rm d} y=\frac{A\cdot \rm 4}{\pi}\hspace{0.3cm} \rightarrow\hspace{0.3cm} A=\frac{\pi}{\rm 4} \hspace{0.15cm}\underline{= \rm 0.785}.$$


(3)  The transformation formula can be described as follows:

$$f_y(y)=\frac{f_x(x)}{| g'(x)|}\Big|_{\, x=h(y)}=f_x(x)\cdot |h'(y)| \Big|_{\, x=h(y)}.$$
  • The inverse function  $x = h(y)$  of a monotonically increasing characteristic  $y = g(x)$  also increases monotonically.
  • Therefore one does not need to make use of the absolute value and subsequently obtains:
$$h\hspace{0.05cm}'(y)=\frac{f_y(y)}{f_x(x)\Big|_{\, x=h(y)}}={\pi}/{\rm 2}\cdot \cos({\pi}/{2}\cdot y).$$
  • At the point  $y = 0$  the slope has the value  $h\hspace{0.05cm}'(y= 0)=π/2\hspace{0.15cm}\underline{\approx 1.571}$.


(4)  One obtains by (indefinite) integration:

$$h(y)=\int h\hspace{0.05cm}'(y)\, {\rm d} y + C = \frac{\pi}{2}\cdot \frac{2}{\pi}\cdot \sin(\frac{\pi}{ 2}\cdot y) + C.$$
  • The constraint  $h(y= 0) = 0$  leads to the constant  $C = 0$  and thus to the result:
$$h(y) = \sin({\pi}/{2}\cdot y) \hspace{0.5cm} \rightarrow\hspace{0.5cm} h(y = 1) \hspace{0.15cm}\underline{= +1}.$$


(5)  The inverse function of the function determined in subtask  (4)  is  $x = h(y)$  :

$$y=g(x)={\rm 2}/{\rm \pi}\cdot \rm arcsin({\it x}).$$
  • This characteristic curve increases monotonically in the range  $-1 \le x \le +1$  from  $y = -1$  to  $y = +1$ .
  • So the value we are looking for is  $g(x= 1) \hspace{0.15cm}\underline{= +1}$.