Difference between revisions of "Aufgaben:Exercise 4.7Z: Generation of a Joint PDF"
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− | {{quiz-Header|Buchseite= | + | {{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Linear_Combinations_of_Random_Variables |
}} | }} | ||
− | [[File:P_ID423__Sto_Z_4_7.png|right|frame| | + | [[File:P_ID423__Sto_Z_4_7.png|right|frame|Requirements for the generation of a <br>two-dimensional random variable]] |
− | + | Given statistically independent quantities $u$ and $v$, | |
− | + | *both of which are uniformly distributed between $-1$ and $+1$, and | |
− | + | *thus each have variance $\sigma^2 = 2/3$, | |
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
+ | generate a two-dimensional random variable $(x,\hspace{0.08cm} y)$ where for the components: | ||
+ | :$$x = A \cdot u + B \cdot v + C,$$ | ||
+ | :$$y= D \cdot u + E \cdot v + F.$$ | ||
+ | The two-dimensional random variable $(x,\hspace{0.08cm} y)$ to be generated should have the following statistical properties: | ||
+ | * Let the variances be $\sigma_x^2 = 4$ and $\sigma_y^2 = 10$. | ||
+ | * Let the random variable $x$ be mean-free $(m_x =0)$. | ||
+ | * For the mean of $y$ let $m_y = 1$ hold. | ||
+ | * The correlation coefficient between $x$ and $y$ is $\rho_{xy} = \sqrt{0.9} = 0.949.$ | ||
+ | * The random variable $x$ possess a triangular PDF $f_x(x)$ corresponding to the above graph. | ||
+ | * The random variable $y$ has a trapezoidal PDF $f_y(y)$ according to the lower graph. | ||
− | + | Hints: | |
− | + | *The exercise belongs to the chapter [[Theory_of_Stochastic_Signals/Linear_Combinations_of_Random_Variables|Linear Combinations of Random Variables]]. | |
− | + | *In particular, reference is made to the page [[Theory_of_Stochastic_Signals/Linear_Combinations_of_Random_Variables#Generation_of_correlated_random_variables|Generation of correlated random variables]]. | |
− | * | + | *To avoid ambiguity, it is specified that all coefficients $A$, ... , $F$ should be non-negative. |
− | * | ||
− | * | ||
− | === | + | ===Questions=== |
<quiz display=simple> | <quiz display=simple> | ||
− | { | + | {Determine the coefficients $C$ and $F$. |
|type="{}"} | |type="{}"} | ||
$C \ = \ $ { 0. } | $C \ = \ $ { 0. } | ||
Line 39: | Line 40: | ||
− | { | + | {Determine the coefficients $A$ and $B$. |
|type="{}"} | |type="{}"} | ||
$A \ = \ $ { 1.732 3% } | $A \ = \ $ { 1.732 3% } | ||
Line 45: | Line 46: | ||
− | { | + | {Determine the coefficients $D$ and $E$, where $D > E$ should hold. |
|type="{}"} | |type="{}"} | ||
$D \ = \ $ { 3.464 3% } | $D \ = \ $ { 3.464 3% } | ||
Line 51: | Line 52: | ||
− | { | + | {Specify the maximum values for $x$ and $y$. |
|type="{}"} | |type="{}"} | ||
$x_\text{max}\ = \ $ { 3.464 3% } | $x_\text{max}\ = \ $ { 3.464 3% } | ||
$y_\text{max}\ = \ $ { 6.196 3% } | $y_\text{max}\ = \ $ { 6.196 3% } | ||
+ | |||
</quiz> | </quiz> | ||
− | === | + | ===Solution=== |
{{ML-Kopf}} | {{ML-Kopf}} | ||
− | '''(1)''' | + | '''(1)''' Given the mean values, it must hold: |
:$$ C = m_x\hspace{0.15cm}\underline{ = 0},$$ | :$$ C = m_x\hspace{0.15cm}\underline{ = 0},$$ | ||
:$$ F = m_y\hspace{0.15cm}\underline{ = 1}.$$ | :$$ F = m_y\hspace{0.15cm}\underline{ = 1}.$$ | ||
− | |||
− | |||
− | * | + | '''(2)''' Taking into account $\sigma^2 = 2/3$ holds: |
− | * | + | :$$\sigma_x^2 = \sigma^2 \cdot ( A^2 + B^2)= {2}/{3} \cdot ( A^2 + B^2) .$$ |
− | * | + | |
+ | *Because of $\sigma_x^2 = 4$ it follows $A^2 + B^2= 6$. | ||
+ | *A triangular PDF means that $A = \pm B$ must hold. | ||
+ | *Thus, since negative coefficients have been excluded, we obtain: | ||
:$$ A = B = \sqrt{3}\hspace{0.15cm}\underline{ = 1.732}.$$ | :$$ A = B = \sqrt{3}\hspace{0.15cm}\underline{ = 1.732}.$$ | ||
+ | |||
− | [[File:P_ID424__Sto_Z_4_7_d.png|right|frame| | + | [[File:P_ID424__Sto_Z_4_7_d.png|right|frame|Rhombic joint PDF]] |
− | '''(3)''' | + | '''(3)''' With $ A = B = \sqrt{3}$ corresponding to the last subtask, two equations of determination remain for $D$ and $E$: |
− | :$$\sigma_y^2 = | + | :$$\sigma_y^2 = \sigma^2 \cdot ( D^2 + E^2)= 10 \hspace{0.5cm} \Rightarrow \hspace{0.5cm} D^2 + E^2 = \frac {\sigma_y^2}{\sigma^2} = \frac {10}{2/3} \stackrel{!}{=}15,$$ |
− | :$$\rho_{xy} = \frac{A \cdot D + B \cdot E}{\sqrt{(A^2 + B^2)(D^2 + E^2)}} = \frac{\sqrt{3} \cdot (D + | + | :$$\rho_{xy} = \frac{A \cdot D + B \cdot E}{\sqrt{(A^2 + B^2)(D^2 + E^2)}} = \frac{\sqrt{3} \cdot (D + E)}{\sqrt{6 \cdot (D^2 + E^2)}} \stackrel{!}{=} \sqrt{0.9}.$$ |
− | * | + | *From this it further follows: $D + E = \sqrt{1.8 \cdot ( D^2 + E^2)} = \sqrt{27} = 3 \cdot \sqrt{3}.$ |
− | * | + | *The equation, in conjunction with $D^2 + E^2 = 15$ and the constraint $(D>E)$ leads to the result: |
:$$ D= 2 \cdot \sqrt{3}\hspace{0.15cm}\underline{ = 3.464}, \hspace{0.5cm}E= \sqrt{3} \hspace{0.15cm}\underline{= 1.732}.$$ | :$$ D= 2 \cdot \sqrt{3}\hspace{0.15cm}\underline{ = 3.464}, \hspace{0.5cm}E= \sqrt{3} \hspace{0.15cm}\underline{= 1.732}.$$ | ||
− | '''(4)''' | + | |
− | :$$ x_\text{max}= A+B \hspace{0.15cm}\underline{ = +3.464}, \hspace{0.5cm} x_\text{min} = - | + | '''(4)''' The random variables $x$ and $y$ resp. take their maximum values when $u= +1$ and $v= +1$ holds: |
+ | :$$ x_\text{max}= A+B \hspace{0.15cm}\underline{ = +3.464}, \hspace{0.5cm} x_\text{min} = - A - B= -3.464.$$ | ||
:$$ y_\text{max}= D+E+F \hspace{0.15cm}\underline{ = +6.196}, \hspace{0.5cm} y_\text{min} = -D-E+F= -4.196.$$ | :$$ y_\text{max}= D+E+F \hspace{0.15cm}\underline{ = +6.196}, \hspace{0.5cm} y_\text{min} = -D-E+F= -4.196.$$ | ||
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− | [[Category: | + | [[Category:Theory of Stochastic Signals: Exercises|^4.3 Linear Combinations^]] |
Latest revision as of 17:45, 25 February 2022
Given statistically independent quantities $u$ and $v$,
- both of which are uniformly distributed between $-1$ and $+1$, and
- thus each have variance $\sigma^2 = 2/3$,
generate a two-dimensional random variable $(x,\hspace{0.08cm} y)$ where for the components:
- $$x = A \cdot u + B \cdot v + C,$$
- $$y= D \cdot u + E \cdot v + F.$$
The two-dimensional random variable $(x,\hspace{0.08cm} y)$ to be generated should have the following statistical properties:
- Let the variances be $\sigma_x^2 = 4$ and $\sigma_y^2 = 10$.
- Let the random variable $x$ be mean-free $(m_x =0)$.
- For the mean of $y$ let $m_y = 1$ hold.
- The correlation coefficient between $x$ and $y$ is $\rho_{xy} = \sqrt{0.9} = 0.949.$
- The random variable $x$ possess a triangular PDF $f_x(x)$ corresponding to the above graph.
- The random variable $y$ has a trapezoidal PDF $f_y(y)$ according to the lower graph.
Hints:
- The exercise belongs to the chapter Linear Combinations of Random Variables.
- In particular, reference is made to the page Generation of correlated random variables.
- To avoid ambiguity, it is specified that all coefficients $A$, ... , $F$ should be non-negative.
Questions
Solution
(1) Given the mean values, it must hold:
- $$ C = m_x\hspace{0.15cm}\underline{ = 0},$$
- $$ F = m_y\hspace{0.15cm}\underline{ = 1}.$$
(2) Taking into account $\sigma^2 = 2/3$ holds:
- $$\sigma_x^2 = \sigma^2 \cdot ( A^2 + B^2)= {2}/{3} \cdot ( A^2 + B^2) .$$
- Because of $\sigma_x^2 = 4$ it follows $A^2 + B^2= 6$.
- A triangular PDF means that $A = \pm B$ must hold.
- Thus, since negative coefficients have been excluded, we obtain:
- $$ A = B = \sqrt{3}\hspace{0.15cm}\underline{ = 1.732}.$$
(3) With $ A = B = \sqrt{3}$ corresponding to the last subtask, two equations of determination remain for $D$ and $E$:
- $$\sigma_y^2 = \sigma^2 \cdot ( D^2 + E^2)= 10 \hspace{0.5cm} \Rightarrow \hspace{0.5cm} D^2 + E^2 = \frac {\sigma_y^2}{\sigma^2} = \frac {10}{2/3} \stackrel{!}{=}15,$$
- $$\rho_{xy} = \frac{A \cdot D + B \cdot E}{\sqrt{(A^2 + B^2)(D^2 + E^2)}} = \frac{\sqrt{3} \cdot (D + E)}{\sqrt{6 \cdot (D^2 + E^2)}} \stackrel{!}{=} \sqrt{0.9}.$$
- From this it further follows: $D + E = \sqrt{1.8 \cdot ( D^2 + E^2)} = \sqrt{27} = 3 \cdot \sqrt{3}.$
- The equation, in conjunction with $D^2 + E^2 = 15$ and the constraint $(D>E)$ leads to the result:
- $$ D= 2 \cdot \sqrt{3}\hspace{0.15cm}\underline{ = 3.464}, \hspace{0.5cm}E= \sqrt{3} \hspace{0.15cm}\underline{= 1.732}.$$
(4) The random variables $x$ and $y$ resp. take their maximum values when $u= +1$ and $v= +1$ holds:
- $$ x_\text{max}= A+B \hspace{0.15cm}\underline{ = +3.464}, \hspace{0.5cm} x_\text{min} = - A - B= -3.464.$$
- $$ y_\text{max}= D+E+F \hspace{0.15cm}\underline{ = +6.196}, \hspace{0.5cm} y_\text{min} = -D-E+F= -4.196.$$