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
 
}}
 
}}
  
[[File:P_ID143__Sto_A_3_1.png|right|]]
+
[[File:P_ID143__Sto_A_3_1.png|right|frame|Cosine&ndash;square PDF&nbsp; (top),&nbsp; and <br>Dirac delta PDF&nbsp; (bottom)]]
:Die Grafik zeigt die Wahrscheinlichkeitsdichtefunktionen (WDF) zweier Zufallsgr&ouml;&szlig;en <i>x</i> und <i>y</i>.
+
The graph shows the probability density functions&nbsp; $\rm (PDF)$&nbsp; of two random variables&nbsp; $x$&nbsp; and&nbsp; $y$.
  
:Die WDF der Zufallsgr&ouml;&szlig;e <i>x</i> lautet in analytischer Form:
+
*The PDF of the random variable&nbsp; $x$&nbsp; in analytical form is:
:$$f_x(x)=\left\{\begin{array}{*{4}{c}}A\rm \cdot \rm cos^2(\frac{\pi}{4}\cdot \it x)  &\rm f\ddot{u}r\hspace{0.1cm} -2\le \it x\le \rm 2, \\0 & \rm sonst.  \\\end{array}\right.$$
+
:$$f_x(x)=\left\{\begin{array}{*{4}{c}}A \cdot \cos^2({\pi}/{4}\cdot x)  &\rm for\hspace{0.1cm} -2\le \it x\le \rm +2, \\0 & \rm else.  \\\end{array}\right.$$
  
:Dagegen besteht die WDF der Zufallsgr&ouml;&szlig;e <i>y</i> aus insgesamt f&uuml;nf Diracfunktionen mit den im Bild unten angegebenen Gewichten.
+
*The PDF of the random variable&nbsp; $y$&nbsp; consists of a total of five Dirac delta functions with the weights given in the graph.
  
:Betrachtet man diese Zufallsgr&ouml;&szlig;en als Momentanwerte zweier Zufallssignale <i>x</i>(<i>t</i>) und <i>y</i>(<i>t</i>), so ist offensichtlich, dass beide Signale auf den Bereich &plusmn;2 „amplitudenbegrenzt“ sind. Betragsmäßig größere Werte kommen nicht vor.<br /><br /><br />
 
  
:<b>Hinweis</b>: Die Aufgabe bezieht sich auf den gesamten Lehrstoff von Kapitel 2.1 und Kapitel 3.1. Es gilt folgende Gleichung:
+
If we consider these random variables as instantaneous values of two random signals&nbsp; $x(t)$&nbsp; and&nbsp; $y(t)$, <br>it is obvious that both signals are&nbsp; "amplitude limited"&nbsp; to the range&nbsp; $\pm 2$.&nbsp;.  
:$$\int \rm cos^{\rm 2}(\it ax)\, {\rm d}x=\frac{\it x}{\rm 2}+\frac{\rm 1}{\rm 4 \it a}\cdot \rm sin(\rm 2\it ax).$$
 
  
  
===Fragebogen===
+
 
 +
 
 +
 
 +
Hints:
 +
*The exercise belongs to the chapter&nbsp; [[Theory_of_Stochastic_Signals/Probability_Density_Function|Probability Density Function]].
 +
*Reference is also made to the chapter&nbsp; [[Theory_of_Stochastic_Signals/From_Random_Experiment_to_Random_Variable|From Random Experiment to Random Variable]].
 +
*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===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welche der nachfolgenden Aussagen treffen uneingeschr&auml;nkt zu?
+
{Which of the following statements are absolutely true?
 
|type="[]"}
 
|type="[]"}
+ Die Zufallsgr&ouml;&szlig;e <i>x</i> ist wertkontinuierlich.
+
+ The random variable&nbsp; $x$&nbsp; is continuous in value.
+ Die Zufallsgr&ouml;&szlig;e <i>y</i> ist wertdiskret.
+
+ The random variable&nbsp; $y$&nbsp; is discrete in value.
- Die Zufallsgr&ouml;&szlig;e <i>y</i> ist gleichzeitig zeitdiskret.
+
- The randomness of&nbsp; $y$&nbsp; is also discrete in time.
+ Die WDF sagt nichts aus bzgl. &bdquo;zeitdiskret/zeitkontinuierlich&rdquo;.
+
+ The PDF says nothing regarding&nbsp; "discrete-time/continuous-time."
  
  
{Berechnen Sie den Parameter <i>A</i> der WDF <i>f<sub>x</sub>(x)</i>.
+
{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 <i>x</i> exakt gleich 0 ist?
+
{What is the probability that&nbsp; $x = 0$&nbsp;  holds?
 
|type="{}"}
 
|type="{}"}
$Pr(x\ =\ 0)$ = { 0 3% }
+
${\rm Pr}(x = 0)\ = \ $ { 0. }
  
  
{Wie groß ist die Wahrscheinlichkeit, dass <i>x</i> größer als 0 ist?
+
{What is the probability that&nbsp; $x > 0$&nbsp;?
 
|type="{}"}
 
|type="{}"}
$Pr(x\ >\ 0)$ = { 0.5 3% }
+
${\rm Pr}(x > 0)\ = \ $ { 0.5 3% }
  
  
{Wie gro&szlig; ist die Wahrscheinlichkeit, dass <i>y</i> gr&ouml;&szlig;er als 0 ist?
+
{What is the probability that&nbsp; $y > 0$&nbsp;?
 
|type="{}"}
 
|type="{}"}
$Pr(y\ >\ 0)$ = { 0.3 3% }
+
${\rm Pr}(y > 0)\ = \ $ { 0.3 3% }
  
  
{Wie gro&szlig; ist die Wahrscheinlichkeit, dass <i>y</i> betragsm&auml;&szlig;ig kleiner als 1 ist?
+
{What is the probability that&nbsp; $|\hspace{0.05cm}y\hspace{0.05cm}|$&nbsp; is smaller than&nbsp; $1$&nbsp;?
 
|type="{}"}
 
|type="{}"}
$Pr(|y|\ <\ 1)$ = { 0.4 3% }
+
${\rm Pr}(|\hspace{0.05cm}y\hspace{0.05cm}| <1)\ = \ $ { 0.4 3% }
  
  
{Wie gro&szlig; ist die Wahrscheinlichkeit, dass <i>x</i> betragsm&auml;&szlig;ig kleiner als 1 ist?
+
{What is the probability that&nbsp; $|\hspace{0.05cm}x\hspace{0.05cm}|$&nbsp; is smaller than &nbsp; $1$&nbsp;?
 
|type="{}"}
 
|type="{}"}
$Pr(|x|\ <\ 1)$ = { 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}}
:<b>1.</b>&nbsp;&nbsp;Richtig sind die <u>Aussagen 1, 2 und 4</u>: <i>x</i> ist wertkontinuierlich und <i>y</i> wertdiskret (<i>M</i> = 5). Die WDF liefert keine Aussagen dar&uuml;ber, ob eine Zufallsgr&ouml;&szlig;e zeitdiskret oder zeitkontinuierlich ist.
+
'''(1)'''&nbsp; Correct are&nbsp; <u>statements 1, 2, and 4</u>:
 +
* $x$&nbsp; is  continuous in value.
 +
* $y$&nbsp; is discrete in value&nbsp; $(M = 5)$.
 +
*The PDF does not provide information about whether a random variable is discrete or continuous in time.
 +
 
 +
 
 +
 
 +
[[File:EN_Sto_A_3_1_b.png|right|frame|For calculating the PDF area]]
 +
'''(2)'''&nbsp; The area under the PDF must&nbsp; yield $1$:
 +
*By simple geometric reasoning, &nbsp; one arrives at the result $\underline{A=0.5}$.
 +
 
  
[[File:P_ID174__Sto_A_3_1_b.png|right|]]
 
: <b>2.</b>&nbsp;&nbsp;Die Fl&auml;che unter der WDF muss 1 ergeben. Durch einfache geometrische &Uuml;berlegungen kommt man zum Ergebnis <u><i>A</i> = 0.5</u>.
 
<br><br><br><br><br><br><br>
 
  
:<b>3.</b>&nbsp;&nbsp;Die Wahrscheinlichkeit, dass die wertkontinuierliche Zufallsgr&ouml;&szlig;e <i>x</i> einen festen Wert <i>x</i><sub>0</sub> annimmt, ist stets vernachl&auml;ssigbar klein &nbsp;&#8658;&nbsp; <u>Pr(<i>x</i> = 0) = 0</u>. F&uuml;r die wertdiskrete Zufallsgr&ouml;&szlig;e <i>y</i> gilt dagegen gemäß der Angabe: Pr(<i>y</i> = 0) = 0.4 (Gewicht der Diracfunktion bei <i>y</i> = 0).
+
'''(3)'''&nbsp; The probability that the continuous-valued random variable&nbsp; $x$&nbsp; takes a fixed value&nbsp; $x_0$&nbsp; is always negligibly small:
 +
:$$\underline{{\rm Pr}(x = 0) = 0}.$$
 +
*On the other hand,&nbsp; for the discrete value random variable&nbsp; $y$&nbsp; holds according to the specification: &nbsp;
 +
:$${\rm Pr}(y = 0) = 0.4,$$
 +
:because the given weight of the Dirac delta function at&nbsp; $y = 0$&nbsp; is&nbsp;$0.4$.
  
:<b>4.</b>&nbsp;&nbsp;Wegen Pr(<i>x</i> = 0) und der WDF-Symmetrie ergibt sich Pr(<i>x</i> > 0) <u>= 0.5</u>.
 
  
:<b>5.</b>&nbsp;&nbsp;Da <i>y</i> eine diskrete Zufallsgr&ouml;&szlig;e ist, addieren sich die Wahrscheinlichkeiten f&uuml;r <i>y</i> = 1 und <i>y</i> = 2:
 
:$$\rm Pr(\it y >\rm 0) =  \rm Pr(\it y = \rm 1) + \rm Pr(\it y = \rm 2) \hspace{0.15cm}\underline {= 0.3}.$$
 
  
:<b>6.</b>&nbsp;&nbsp;Das Ereignis &bdquo;| <i>y</i> | < 1&rdquo; ist hier identisch mit &bdquo;<i>y</i> = 0&rdquo;. Damit erh&auml;lt man:
+
'''(4)'''&nbsp; Because of&nbsp; ${{\rm Pr}(x = 0) = 0}$&nbsp; and the PDF symmetry,&nbsp; we get&nbsp; $\underline{{\rm Pr}(x > 0) = 0.5}$.
:$$\rm Pr(|\it y| < \rm 1) =  \rm Pr(\it y = \rm 0)\hspace{0.15cm}\underline {  = 0.4}.$$
 
  
:<b>7.</b>&nbsp;&nbsp;Die gesuchte Wahrscheinlichkeit ist gleich dem Integral von -1 bis +1 &uuml;ber die WDF der kontinuierlichen Zufallsgr&ouml;&szlig;e <i>x</i>. Unter Ber&uuml;cksichtigung der Symmetrie und der angegebenen Gleichung erh&auml;lt man:
+
 
:$$\rm Pr(|\it x|<\rm 1)=\rm 2 \cdot \int_{0}^{1}\frac{1}{2}\cdot cos^2(\frac{\pi}{4}\cdot \it x)\hspace{0.1cm}{\rm d}x=\frac{x}{\rm 2}+\frac{\rm 1}{\pi}\cdot\rm sin(\frac{\pi}{2}\cdot\it x)\Bigg |_{\rm 0}^{\rm 1}=\rm\frac{1}{2} + \frac{1}{\pi}
+
 
 +
'''(5)'''&nbsp; Since&nbsp; $y$&nbsp; is a discrete random variable,&nbsp; the probabilities for&nbsp; $y = 1$&nbsp; and&nbsp; $y = 2$&nbsp; add up:
 +
:$${\rm Pr}(y >0) = {\rm Pr}(y = 1) + {\rm Pr}( y = 2) \hspace{0.15cm}\underline {= 0.3}.$$
 +
 
 +
 
 +
 
 +
'''(6)'''&nbsp; The event&nbsp; $|\hspace{0.05cm} y \hspace{0.05cm} | < 1$&nbsp; here is identical to &nbsp;$y = 0$. Thus we obtain:
 +
:$${\rm Pr}(|\hspace{0.05cm}y\hspace{0.05cm}| < 1) = {\rm Pr}( y = 0)\hspace{0.15cm}\underline { = 0.4}.$$
 +
 
 +
 
 +
'''(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; $x$.  
 +
*Taking into account the symmetry and the given equation,&nbsp; 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{
 
\hspace{0.15cm}\underline{
 
\approx 0.818}.$$
 
\approx 0.818}.$$
 
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
  
[[Category:Aufgaben zu Stochastische Signaltheorie|^3.1 Wahrscheinlichkeitsdichtefunktion^]]
+
[[Category:Theory of Stochastic Signals: Exercises|^3.1 Probability Density Function^]]

Latest revision as of 17:53, 8 February 2022

Cosine–square PDF  (top),  and
Dirac delta PDF  (bottom)

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

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



Hints:

$$\int \cos^{\rm 2}( ax)\, {\rm d}x=\frac{x}{2}+\frac{1}{4 a}\cdot \sin(2 ax).$$


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  $x = 0$  holds?

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

4

What is the probability that  $x > 0$ ?

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

5

What is the probability that  $y > 0$ ?

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

6

What is the probability that  $|\hspace{0.05cm}y\hspace{0.05cm}|$  is smaller than  $1$ ?

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

7

What is the probability that  $|\hspace{0.05cm}x\hspace{0.05cm}|$  is smaller than   $1$ ?

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


Solution

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

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


For calculating the PDF area

(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 variable  $x$  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:  
$${\rm Pr}(y = 0) = 0.4,$$
because the given weight of the Dirac delta function at  $y = 0$  is $0.4$.


(4)  Because of  ${{\rm Pr}(x = 0) = 0}$  and the 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:
$${\rm Pr}(|\hspace{0.05cm} x\hspace{0.05cm}|<1)=2 \cdot \int_{0}^{1}{1}/{2}\cdot \cos^2({\pi}/{4}\cdot x)\hspace{0.1cm}{\rm d}x={x}/{2}+{1}/{\pi}\cdot \sin({\pi}/{2}\cdot x)\Big |_{\rm 0}^{\rm 1}=\rm{1}/{2} + {1}/{\pi} \hspace{0.15cm}\underline{ \approx 0.818}.$$