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| | + | [[File:P_ID136__Sto_A_3_9.png|right|frame|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 $x$ : | |
:$$f_y(y)=A\cdot\cos({\pi}/{2}\cdot y).$$ | :$$f_y(y)=A\cdot\cos({\pi}/{2}\cdot y).$$ | ||
− | * | + | *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 opposite. |
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− | + | Hints: | |
− | + | *The exercise belongs to the chapter [[Theory_of_Stochastic_Signals/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]]. |
− | * | ||
− | === | + | ===Question=== |
<quiz display=simple> | <quiz display=simple> | ||
− | { | + | {Which of the following statements are true? |
|type="[]"} | |type="[]"} | ||
− | + | + | + Outside the range $-1 \le x \le +1$ can be $g(x)$ arbitrary. |
− | - | + | - The characteristic curve must be symmetrical about $x= 0$ : $g(-x) = g(x)$. |
− | + | + | + The random variable $y$ has a smaller variance than $x$. |
− | { | + | {Calculate the $f_y(y)$–value at $y = 0$: $A = f_y(0)$. |
|type="{}"} | |type="{}"} | ||
− | $A \ = \ $ | + | $A \ = \ $ { 0.785 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 $y = 0$ ? |
|type="{}"} | |type="{}"} | ||
$h'(y = 0) \ = \ $ { 1.571 3% } | $h'(y = 0) \ = \ $ { 1.571 3% } | ||
− | { | + | {Compute with the result from '''(3)''' the function $x = h(y)$ under the constraint $h(0) = 0$. What value results für $y = 1$ ? |
|type="{}"} | |type="{}"} | ||
$h(y=1) \ = \ $ { 1 3% } | $h(y=1) \ = \ $ { 1 3% } | ||
− | { | + | {Determine the function progression $y = g(x)$ of the characteristic we are looking for. What is the function value at the point $x = 1$ ? |
|type="{}"} | |type="{}"} | ||
$g(x = 1) \ = \ $ { 1 3% } | $g(x = 1) \ = \ $ { 1 3% } | ||
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</quiz> | </quiz> | ||
− | === | + | ===Solution=== |
{{ML-Kopf}} | {{ML-Kopf}} | ||
− | '''(1)''' | + | |
− | * | + | '''(1)''' Correct are <u>statements 1 and 3</u>: |
− | * | + | *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 $\sigma_y^2 < \sigma_x^2$ is. | ||
− | '''(2)''' | + | '''(2)''' The integral over the PDF must always equal $1$ . It follows that: |
− | :$$\int_{-\rm 1}^{\rm 1}A\cdot \cos({\pi}/{\rm 2}\cdot y)\, {\rm d} y=\frac{A\cdot \rm 4}{\pi}\hspace{0.3cm} \ | + | :$$\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}.$$ |
− | '''(3)''' | + | '''(3)''' The transformation formula can be transformed as follows: |
:$$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)}.$$ | ||
− | * | + | *The inverse function $x = h(y)$ of a monotonically increasing characteristic $y = g(x)$ also increases monotonically. |
− | * | + | *Therefore the magnitude formation can be omitted and one obtains: |
− | :$$h\hspace{0.05cm}'(y)=\frac{f_y(y)}{f_x(x)\Big|_{\, x=h(y)}}={\pi}/{\rm 2}\cdot \cos({\pi}/{2}\cdot | + | :$$h\hspace{0.05cm}'(y)=\frac{f_y(y)}{f_x(x)\Big|_{\, x=h(y)}}={\pi}/{\rm 2}\cdot \cos({\pi}/{2}\cdot y).$$ |
− | * | + | *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)''' | + | '''(4)''' One obtains by (indefinite) integration: |
− | :$$h(y)=\int h\hspace{0.05cm}'(y)\, {\rm d} y + C = \frac{\pi}{2}\cdot \frac{2}{\pi}\cdot \sin(\frac{\pi}{ 2}\cdot | + | :$$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} \ | + | :$$h(y) = \sin({\pi}/{2}\cdot y) \hspace{0.5cm} \rightarrow\hspace{0.5cm} |
− | h(y = 1) | + | h(y = 1) \hspace{0.15cm}\underline{= +1}.$$ |
− | '''(5)''' | + | '''(5)''' The inverse function of the function determined in subtask '''(4)''' is $x = h(y)$ : |
:$$y=g(x)={\rm 2}/{\rm \pi}\cdot \rm arcsin({\it x}).$$ | :$$y=g(x)={\rm 2}/{\rm \pi}\cdot \rm arcsin({\it x}).$$ | ||
− | * | + | *This characteristic increases monotonically in the range $-1 \le x \le +1$ from $y = -1$ to $y = +1$ . |
− | * | + | *So the value we are looking for is $g(x= 1) \hspace{0.15cm}\underline{= +1}$. |
{{ML-Fuß}} | {{ML-Fuß}} |
Revision as of 12:52, 12 January 2022
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 opposite.
Hints:
- The exercise belongs to the chapter Theory of Stochastic Signals/Exponentially Distributed Random Variables.
- In particular, reference is made to the page Transformation of random variables.
Question
Solution
(1) Correct are 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 $\sigma_y^2 < \sigma_x^2$ is.
(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 transformed 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 the magnitude formation can be omitted and one 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 increases monotonically in the range $-1 \le x \le +1$ from $y = -1$ to $y = +1$ .
- So the value we are looking for is $g(x= 1) \hspace{0.15cm}\underline{= +1}$.