:Obwohl die Beschreibung Gaußscher Zufallsgrößen mit Hilfe von Vektoren und Matrizen eigentlich nur bei mehr als <i>N</i> = 2 Dimensionen erforderlich ist und Sinn macht, beschränken wir uns hier auf den Sonderfall zweidimensionaler Zufallsgrößen.
Although the description of Gaussian random variables using vectors and matrices is actually only necessary and makes sense for more than $N = 2$ dimensions, here we restrict ourselves to the special case of two-dimensional random variables for simplicity.
:In der Grafik ist oben die allgemeine Korrelationsmatrix <b>K<sub>x</sub></b> der 2D–Zufallsgröße <b>x</b> = (<i>x</i><sub>1</sub>, <i>x</i><sub>2</sub>)<sup>T</sup> angegeben, wobei <i>σ</i><sub>1</sub><sup>2</sup> und <i>σ</i><sub>2</sub><sup>2</sup> die Varianzen der Einzelkomponenten beschreiben. <i>ρ</i> bezeichnet den Korrelationskoeffizienten zwischen den beiden Komponenten.
In the graph above, the general correlation matrix $\mathbf{K_x}$ of the two-dimensional random variable $\mathbf{x} = (x_1, x_2)^{\rm T}$ is given, where $\sigma_1^2$ and $\sigma_2^2$ describe the variances of the individual components. $\rho$ denotes the correlation coefficient between the two components.
:Die Zufallsgrößen <b>y</b> und <b>z</b> geben zwei Spezialfälle von <b>x</b> an, deren Prozessparameter aus den Kovarianzmatrizen <b>K<sub>y</sub></b> und <b>K<sub>z</sub></b> bestimmt werden können.<br>
The random variables $\mathbf{y}$ and $\mathbf{z}$ give two special cases of $\mathbf{x}$ whose process parameters are to be determined from the correlation matrices $\mathbf{K_y}$ and $\mathbf{K_z}$ respectively.
:<b>Hinweis:</b> Die Aufgabe bezieht sich auf die theoretischen Grundlagen von Kapitel 4.7. Einige Grundlagen zur Anwendung von Vektoren und Matrizen finden sich auf den folgenden Seiten:<br> Determinante einer Matrix,<br> Inverse einer Matrix.
:<br><br>Weiterhin ist zu beachten:
:* Eine 2×2-Kovarianzmatrix besitzt zwei reelle Eigenwerte <i>λ</i><sub>1</sub> und <i>λ</i><sub>2</sub>.<br>
:* Die beiden Eigenwerte bestimmen zwei Eigenvektoren <i>ξ</i><sub>1</sub> und <i>ξ</i><sub>2</sub> und diese spannen ein neues Koordinatensystem in Richtung der Hauptachsen des alten Systems auf.
:* Entsprechend der Seite Höhenlinien bei korrelierten Zufallsgrößen ist der Winkel <i>α</i> zwischen dem alten und dem neuen System durch folgende Gleichung gegeben:
*The exercise belongs to the chapter [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables|Generalization to N-Dimensional Random Variables]].
*Some basics on the application of vectors and matrices can be found on the pages [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Determinant_of_a_matrix|Determinant of a Matrix]] and [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Inverse_of_a_matrix|Inverse of a Matrix]] .
* According to the page [[Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables#Contour_lines_for_correlated_random_variables|"Contour lines for correlated random variables"]] the angle $\alpha$ between the old and the new system is given by the following equation:
{Give the eigenvalues of $\mathbf{K_y}$ under the condition $\sigma = 1$ and $0 < \rho < 1$ What values result for $\rho = 0.5 $, assuming $\lambda_1 \ge \lambda_2$?
{By what angle $\alpha$ is the new coordinate system $(\mathbf{\zeta_1}, \ \mathbf{\zeta_2})$ rotated with respect to the original system $(\mathbf{z_1}, \ \mathbf{z_2})$ ?
|type="{}"}
$\alpha \ = \ $ { 26.56 3% } $\ \rm deg$
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</quiz>
</quiz>
===Musterlösung===
===Solution===
{{ML-Kopf}}
{{ML-Kopf}}
'''1.'''
'''(1)''' Correct are the <u>proposed solutions 1 and 2</u>:
'''2.'''
*$\mathbf{K_y}$ is indeed the most general correlation matrix of a two-dimensional random variable with $\sigma_1 = \sigma_2 = \sigma$.
'''3.'''
*The parameter $\rho$ specifies the correlation coefficient. This can take all values between $\pm 1$ including these marginal values.
'''4.'''
'''5.'''
'''6.'''
'''7.'''
'''(2)''' In this case, the governing equation is:
*The coordinate system defined by $\mathbf{\eta_1}$ and $\mathbf{\eta_2}$ is actually in the direction of the main axes of the original system.
*With $\sigma_1 = \sigma_2$ almost always results $($exception: $\rho= 0)$ the rotation angle $\alpha = 45^\circ$.
*This also follows from the equation given in the theory section:
*The eigenvalues $\lambda_1$ and $\lambda_2$ do not denote the standard deviations with respect to the new axes, but the variances.
'''(5)''' By comparing the matrices $\mathbf{K_x}$ and $\mathbf{K_z}$ we get.
Although the description of Gaussian random variables using vectors and matrices is actually only necessary and makes sense for more than $N = 2$ dimensions, here we restrict ourselves to the special case of two-dimensional random variables for simplicity.
In the graph above, the general correlation matrix $\mathbf{K_x}$ of the two-dimensional random variable $\mathbf{x} = (x_1, x_2)^{\rm T}$ is given, where $\sigma_1^2$ and $\sigma_2^2$ describe the variances of the individual components. $\rho$ denotes the correlation coefficient between the two components.
The random variables $\mathbf{y}$ and $\mathbf{z}$ give two special cases of $\mathbf{x}$ whose process parameters are to be determined from the correlation matrices $\mathbf{K_y}$ and $\mathbf{K_z}$ respectively.