Difference between revisions of "Aufgaben:Exercise 4.6: Coordinate Rotation"

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{{quiz-Header|Buchseite=Stochastische Signaltheorie/Zweidimensionale Gaußsche Zufallsgrößen
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{{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables
 
}}
 
}}
  
[[File:P_ID431__Sto_A_4_6_neu.png|right|frame|Koordinatendrehung einer 2D-WDF]]
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[[File:P_ID431__Sto_A_4_6_neu.png|right|frame|Coordinate rotation of a joint PDF]]
Wir betrachten in der Aufgabe eine zweidimensionale Gaußsche Zufallsgröße  $(x, y)$  mit statistisch unabhängigen Komponenten.  Die Streuungen der beiden Komponenten seien  $\sigma_x = 1$  und  $\sigma_y = 2$.
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In the exercise we consider a two-dimensional Gaussian random variable  $(x, y)$  with statistically independent components  Let the standard deviations of the two components be  $\sigma_x = 1$  and  $\sigma_y = 2$.
  
Berechnet werden soll die Wahrscheinlichkeit dafür, dass die zweidimensionale Zufallsgröße  $(x, y)$  innerhalb des grün schraffiert eingezeichneten Bereichs liegt:
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We want to calculate the probability that the two-dimensional random variable $(x, y)$  lies within the shaded area:
 
:$$-C \le x + y \le +C.$$
 
:$$-C \le x + y \le +C.$$
  
Führen Sie zur Lösung eine Koordinatentransformation durch:
+
Perform a coordinate transformation to solve:
 
:$$\xi = \hspace{0.4cm} x +y,$$
 
:$$\xi = \hspace{0.4cm} x +y,$$
 
:$$\eta= -x +y .$$
 
:$$\eta= -x +y .$$
  
Dies entspricht einer Drehung des Koordinatensystems um  $45^\circ$.  
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This corresponds to a rotation of the coordinate system by  $45^\circ$.  
*Aus  $x+y= \pm C$  folgt damit  $\xi=\pm C$.  
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*From  $x+y= \pm C$  it thus follows  $\xi=\pm C$.  
*Die beiden zweidimensionalen Dichtefunktionen lauten dann:
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*The two two-dimensional density functions are then:
 
:$$f_{xy} (x,y) = \frac{1}{4 \pi} \cdot \exp \left [ - ( x^2\hspace {-0.1cm} /2 + y^2\hspace {-0.1cm} /8) \right ] ,$$
 
:$$f_{xy} (x,y) = \frac{1}{4 \pi} \cdot \exp \left [ - ( x^2\hspace {-0.1cm} /2 + y^2\hspace {-0.1cm} /8) \right ] ,$$
:$$f_{\xi\eta} (\xi, \eta) = \frac{1}{2 \pi \cdot \sigma_\xi \cdot \sigma_\eta \cdot \sqrt{1 - \rho_{\xi\eta}^2}} \cdot \exp \left [ - \frac{1}{2 \cdot (1 - \rho_{\xi\eta}^2)} \cdot ( \frac {\xi^2}{\sigma_\xi^2} + \frac {\eta^2}{\sigma_\eta^2 }- 2 \rho_{\xi\eta}\cdot \frac {\xi \cdot \eta}{\sigma_\xi \cdot \sigma_\eta}) \right ] .$$
+
:$$f_{\xi\eta} (\xi, \eta) = \frac{1}{2 \pi \cdot \sigma_\xi \cdot \sigma_\eta \cdot \sqrt{1 - \rho_{\xi\eta}^2}} \cdot \exp \left [ - \frac{1}{2 \cdot (1 - \rho_{\xi\eta}^2)} \cdot ( \frac {\xi^2}{\sigma_\xi^2} + \frac {\eta^2}{\sigma_\eta^2 }- 2 \rho_{\xi\eta}\cdot \frac {\xi \cdot \eta}{\sigma_\xi \cdot \sigma_\eta}) \right ] .$$
  
  
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''Hinweise:''
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Hints:
*Die Aufgabe gehört zum  Kapitel  [[Theory_of_Stochastic_Signals/Zweidimensionale_Gaußsche_Zufallsgrößen|Zweidimensionale Gaußsche Zufallsgrößen]].
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*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables|Two-dimensional Gaussian Random Variables]].
*Bezug genommen wird insbesondere auf die Seite  [[Theory_of_Stochastic_Signals/Zweidimensionale_Gaußsche_Zufallsgrößen#Drehung_des_Koordinatensystems|Drehung des Koordinatensystems]].
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*Reference is also made to the chapter  [[Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables#Rotation_of_the_coordinate_system|Rotation of the coordinate system]].
 
   
 
   
*Gegeben sind die Näherungen  ${\rm Q}(2.3) \approx 0.01$  und  ${\rm Q}(2.6) \approx 0.005$  für das komplementäre Gaußsche Fehlerintegral.
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*Given are the approximations  ${\rm Q}(2.3) \approx 0.01$  and  ${\rm Q}(2.6) \approx 0.005$  for the complementary Gaussian error integral.
*Weitere Informationen zu dieser Thematik liefert das Lernvideo  [[Gaußsche_2D-Zufallsgrößen_(Lernvideo)|Gaußsche 2D-Zufallsgrößen]]:
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*More information on this topic is provided in the learning video  [[Gaußsche_2D-Zufallsgrößen_(Lernvideo)|Gaussian 2D random variables]]:
::Teil 1:   Gaußsche Zufallsgrößen ohne statistische Bindungen,   
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::Part 1:   Gaussian random variables without statistical bindings,   
::Teil 2:   Gaußsche Zufallsgrößen mit statistischen Bindungen.  
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::Part 2:   Gaussian random variables with statistical bindings.  
  
  
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===Fragebogen===
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===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Ermitteln Sie durch Koeffizientenvergleich das Verh&auml;ltnis der beiden Streuungen der neuen Zufallsgr&ouml;&szlig;e&nbsp; $(\xi, \eta)$.  
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{Determine the ratio of the two standard deviations of the new random variables by comparing coefficients $(\xi, \eta)$.  
 
|type="{}"}
 
|type="{}"}
$\sigma_\xi/\sigma_\eta \ = \ $ { 1 3% }
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$\sigma_\xi/\sigma_\eta \ = \ $ { 1 3% }
  
  
{Berechnen Sie die Streuung&nbsp; $\sigma_\xi$&nbsp; und den Korrelationskoeffizienten&nbsp; $\rho_{\xi\eta}$&nbsp; zwischen den neuen Zufallsgrößen&nbsp; $\xi$&nbsp; und&nbsp; $\eta$.
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{Calculate the standard deviation&nbsp; $\sigma_\xi$&nbsp; and correlation coefficient&nbsp; $\rho_{\xi\eta}$&nbsp; between the new random variables&nbsp; $\xi$&nbsp; and&nbsp; $\eta$.
 
|type="{}"}
 
|type="{}"}
$\sigma_\xi \ = \ $ { 2.236 3% }
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$\sigma_\xi \ = \ $ { 2.236 3% }
$\rho_{\xi\eta} \ = \ $ { 0.6 3% }
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$\rho_{\xi\eta} \ = \ $ { 0.6 3% }
  
  
{Berechnen Sie die Wahrscheinlichkeit, dass&nbsp; $ |\hspace{0.05cm}x+y\hspace{0.05cm}| \le C$&nbsp; gilt.&nbsp; Wie gro&szlig; ist&nbsp; $C$&nbsp; zu w&auml;hlen, damit&nbsp; $99\%$&nbsp; aller Gr&ouml;&szlig;en im schraffierten Bereich liegen?
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{Calculate the probability that&nbsp; $ |\hspace{0.05cm}x+y\hspace{0.05cm}| \le C$&nbsp; holds.&nbsp; How large should&nbsp; $C$&nbsp; be chosen so that&nbsp; $99\%$&nbsp; of all sizes are in the shaded area?
 
|type="{}"}
 
|type="{}"}
$C_{99\%} \ = \ $ { 5.814 3% }
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$C_{99\%} \ = \ $ { 5.814 3% }
  
  
 
</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Aus &nbsp;$\xi = x + y$&nbsp; und &nbsp;$\eta = -x + y$&nbsp; folgt direkt:
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'''(1)'''&nbsp; From &nbsp;$\xi = x + y$&nbsp; and &nbsp;$\eta = -x + y$&nbsp; it follows directly:
:$$x = {1}/{2} \cdot ( \xi - \eta ) ,\hspace{0.5cm}y = {1}/{2}\cdot ( \xi +\eta ) .$$
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:$$x = {1}/{2} \cdot ( \xi - \eta ) ,\hspace{0.5cm}y = {1}/{2}\cdot ( \xi +\eta ) .$$
  
*Setzt man diese Werte f&uuml;r den negativen Exponenten ein, so erh&auml;lt man:
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*Substituting these values for the negative exponent, we get:
:$$\frac{x^2}{2} + \frac{y^2}{8} = \frac{1}{8} \cdot ( \xi - \eta )^2 + \frac{1}{32} \cdot ( \xi + \eta )^2.$$
+
:$$\frac{x^2}{2} + \frac{y^2}{8} = \frac{1}{8} \cdot ( \xi - \eta )^2 + \frac{1}{32} \cdot ( \xi + \eta )^2.$$
  
*Ausmultipliziert ergibt dies:
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*Multiplied out, this gives:
:$$\frac{5}{32} \cdot \xi^2 + \frac{5}{32} \cdot \eta^2 - \frac{3}{16} \cdot \xi \cdot \eta .$$
+
:$$\frac{5}{32} \cdot \xi^2 + \frac{5}{32} \cdot \eta^2 - \frac{3}{16} \cdot \xi \cdot \eta .$$
  
*Da die Koeffizienten bei&nbsp; $\xi^2$&nbsp; und&nbsp; $\eta^2$&nbsp; gleich sind, gilt&nbsp; $\sigma_\xi = \sigma_\eta$.&nbsp; &nbsp; <u>Der gesuchte Quotient ist somit 1</u>.
+
*Since the coefficients on&nbsp; $\xi^2$&nbsp; and&nbsp; $\eta^2$&nbsp; are equal, then&nbsp; $\sigma_\xi = \sigma_\eta$.&nbsp; &nbsp; <u>The quotient we are looking for is therefore 1</u>.
  
  
  
'''(2)'''&nbsp; Durch Koeffizientenvergleich erh&auml;lt man für&nbsp; $\sigma_\xi = \sigma_\eta$&nbsp; das Gleichungssystem:
+
'''(2)'''&nbsp; By comparing coefficients, we obtain for&nbsp; $\sigma_\xi = \sigma_\eta$&nbsp; the system of equations:
:$$2 \cdot \sigma_\xi^2 \cdot (1 - \rho_{\xi\eta}^2)= \frac{32}{5},$$
+
:$$2 \cdot \sigma_\xi^2 \cdot (1 - \rho_{\xi\eta}^2)= \frac{32}{5},$$
:$$\frac{\sigma_\xi^2 \cdot (1 - \rho_{\xi\eta}^2)}{\rho_{\xi\eta}}= \frac{16}{3}.$$
+
:$$\frac{\sigma_\xi^2 \cdot (1 - \rho_{\xi\eta}^2)}{\rho_{\xi\eta}}= \frac{16}{3}.$$
  
*Setzt man die erste Gleichung in die zweite ein, so ergibt sich&nbsp; $\rho_{\xi\eta}\hspace{0.15cm}\underline {= 0.6}$&nbsp; und&nbsp; $\sigma_{\xi} = \sqrt{5}\hspace{0.15cm}\underline {= 2.236}$.
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*Substituting the first equation into the second, we get&nbsp; $\rho_{\xi\eta}\hspace{0.15cm}\underline {= 0.6}$&nbsp; and&nbsp; $\sigma_{\xi} = \sqrt{5}\hspace{0.15cm}\underline {= 2.236}$.
  
  
  
'''(3)'''&nbsp; Nach Koordinatentransformation kann man f&uuml;r diese Wahrscheinlichkeit schreiben:
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'''(3)'''&nbsp; After coordinate transformation, we can write f&uuml;r this probability:
:$${\rm Pr} ( | x + y | \le C ) = {\rm Pr} ( | \xi | \le C ) = 1 - 2 \cdot {\rm Pr} ( \xi >C ).$$
+
:$${\rm Pr} ( | x + y | \le C ) = {\rm Pr} ( | \xi | \le C ) = 1 - 2 \cdot {\rm Pr} ( \xi >C ).$$
  
*Mit dem komplement&auml;ren Gau&szlig;schen Fehlerintegral folgt daraus weiter:
+
*With the complementary Gaussian error integral, it further follows:
:$${\rm Pr} ( | x + y | \le C ) = 1 - 2 \cdot {\rm Q} ( {C}/{\sigma_\xi}) = 0.99 \hspace{0.3cm} \Rightarrow \hspace{0.3cm} {\rm Q} ( {C}/{\sigma_\xi}) = 0.005.$$
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:$${\rm Pr} ( | x + y | \le C ) = 1 - 2 \cdot {\rm Q} ( {C}/{\sigma_\xi}) = 0.99 \hspace{0.3cm} \Rightarrow \hspace{0.3cm} {\rm Q} ( {C}/{\sigma_\xi}) = 0.005.$$
  
*Mit dem angegebenen Wert&nbsp; ${\rm Q}(2.6) \approx 0.005$&nbsp; erh&auml;lt man somit das Ergebnis:  
+
*With the given value&nbsp; ${\rm Q}(2.6) \approx 0.005$&nbsp; we thus obtain the result:  
 
:$$C \approx 2.6 \cdot \sigma_{\xi}\hspace{0.15cm}\underline {= 5.814}.$$
 
:$$C \approx 2.6 \cdot \sigma_{\xi}\hspace{0.15cm}\underline {= 5.814}.$$
 
{{ML-Fuß}}
 
{{ML-Fuß}}

Revision as of 23:24, 25 January 2022

Coordinate rotation of a joint PDF

In the exercise we consider a two-dimensional Gaussian random variable  $(x, y)$  with statistically independent components  Let the standard deviations of the two components be  $\sigma_x = 1$  and  $\sigma_y = 2$.

We want to calculate the probability that the two-dimensional random variable $(x, y)$  lies within the shaded area:

$$-C \le x + y \le +C.$$

Perform a coordinate transformation to solve:

$$\xi = \hspace{0.4cm} x +y,$$
$$\eta= -x +y .$$

This corresponds to a rotation of the coordinate system by  $45^\circ$.

  • From  $x+y= \pm C$  it thus follows  $\xi=\pm C$.
  • The two two-dimensional density functions are then:
$$f_{xy} (x,y) = \frac{1}{4 \pi} \cdot \exp \left [ - ( x^2\hspace {-0.1cm} /2 + y^2\hspace {-0.1cm} /8) \right ] ,$$
$$f_{\xi\eta} (\xi, \eta) = \frac{1}{2 \pi \cdot \sigma_\xi \cdot \sigma_\eta \cdot \sqrt{1 - \rho_{\xi\eta}^2}} \cdot \exp \left [ - \frac{1}{2 \cdot (1 - \rho_{\xi\eta}^2)} \cdot ( \frac {\xi^2}{\sigma_\xi^2} + \frac {\eta^2}{\sigma_\eta^2 }- 2 \rho_{\xi\eta}\cdot \frac {\xi \cdot \eta}{\sigma_\xi \cdot \sigma_\eta}) \right ] .$$





Hints:

  • Given are the approximations  ${\rm Q}(2.3) \approx 0.01$  and  ${\rm Q}(2.6) \approx 0.005$  for the complementary Gaussian error integral.
  • More information on this topic is provided in the learning video  Gaussian 2D random variables:
Part 1:   Gaussian random variables without statistical bindings,
Part 2:   Gaussian random variables with statistical bindings.



Questions

1

Determine the ratio of the two standard deviations of the new random variables by comparing coefficients $(\xi, \eta)$.

$\sigma_\xi/\sigma_\eta \ = \ $

2

Calculate the standard deviation  $\sigma_\xi$  and correlation coefficient  $\rho_{\xi\eta}$  between the new random variables  $\xi$  and  $\eta$.

$\sigma_\xi \ = \ $

$\rho_{\xi\eta} \ = \ $

3

Calculate the probability that  $ |\hspace{0.05cm}x+y\hspace{0.05cm}| \le C$  holds.  How large should  $C$  be chosen so that  $99\%$  of all sizes are in the shaded area?

$C_{99\%} \ = \ $


Solution

(1)  From  $\xi = x + y$  and  $\eta = -x + y$  it follows directly:

$$x = {1}/{2} \cdot ( \xi - \eta ) ,\hspace{0.5cm}y = {1}/{2}\cdot ( \xi +\eta ) .$$
  • Substituting these values for the negative exponent, we get:
$$\frac{x^2}{2} + \frac{y^2}{8} = \frac{1}{8} \cdot ( \xi - \eta )^2 + \frac{1}{32} \cdot ( \xi + \eta )^2.$$
  • Multiplied out, this gives:
$$\frac{5}{32} \cdot \xi^2 + \frac{5}{32} \cdot \eta^2 - \frac{3}{16} \cdot \xi \cdot \eta .$$
  • Since the coefficients on  $\xi^2$  and  $\eta^2$  are equal, then  $\sigma_\xi = \sigma_\eta$.    The quotient we are looking for is therefore 1.


(2)  By comparing coefficients, we obtain for  $\sigma_\xi = \sigma_\eta$  the system of equations:

$$2 \cdot \sigma_\xi^2 \cdot (1 - \rho_{\xi\eta}^2)= \frac{32}{5},$$
$$\frac{\sigma_\xi^2 \cdot (1 - \rho_{\xi\eta}^2)}{\rho_{\xi\eta}}= \frac{16}{3}.$$
  • Substituting the first equation into the second, we get  $\rho_{\xi\eta}\hspace{0.15cm}\underline {= 0.6}$  and  $\sigma_{\xi} = \sqrt{5}\hspace{0.15cm}\underline {= 2.236}$.


(3)  After coordinate transformation, we can write für this probability:

$${\rm Pr} ( | x + y | \le C ) = {\rm Pr} ( | \xi | \le C ) = 1 - 2 \cdot {\rm Pr} ( \xi >C ).$$
  • With the complementary Gaussian error integral, it further follows:
$${\rm Pr} ( | x + y | \le C ) = 1 - 2 \cdot {\rm Q} ( {C}/{\sigma_\xi}) = 0.99 \hspace{0.3cm} \Rightarrow \hspace{0.3cm} {\rm Q} ( {C}/{\sigma_\xi}) = 0.005.$$
  • With the given value  ${\rm Q}(2.6) \approx 0.005$  we thus obtain the result:
$$C \approx 2.6 \cdot \sigma_{\xi}\hspace{0.15cm}\underline {= 5.814}.$$