Can the Expected Value be Written as an Integral?

In summary, the conversation discusses an expression involving an exponentially distributed random variable and constants, and how it can be written as an integral. The conversation also brings up a possible typo in the original expression and discusses the correct form of the integral. However, the conversation concludes that the expression given by the authors is consistent and does not contain any typos.
  • #1
EngWiPy
1,368
61
Hello,

I have the following expression:

[tex]G^2=\frac{\mathcal{E}_2}{\mathcal{E}_1\,\alpha+\mathcal{N}}[/tex]

where [tex]\alpha[/tex] is an exponentially distributed random variable, and all other variables are constants. The authors said that, this expected value can be written as:

[tex]G^2=\int_0^{\infty}\frac{\mathcal{E}_2}{\mathcal{N}(\gamma+1)}\frac{1}{\overline{\gamma}}\text{e}^{-\gamma/\overline{\gamma}}\,d\gamma[/tex]

Is this right, or there are typos?

Regards
 
Physics news on Phys.org
  • #2
S_David said:
Hello,

I have the following expression:

[tex]G^2=\frac{\mathcal{E}_2}{\mathcal{E}_1\,\alpha+\mathcal{N}}[/tex]

where [tex]\alpha[/tex] is an exponentially distributed random variable, and all other variables are constants. The authors said that, this expected value can be written as:

[tex]G^2=\int_0^{\infty}\frac{\mathcal{E}_2}{\mathcal{N}(\gamma+1)}\frac{1}{\overline{\gamma}}\text{e}^{-\gamma/\overline{\gamma}}\,d\gamma[/tex]

Is this right, or there are typos?

Regards

It looks like something is missing. You have a constant (E1) in the first expression which is not in the second, while you have a constant in the second (gamma bar) which is not in the first.
 
  • #3
Here

[tex]
G^2=\int_0^{\infty}\frac{\mathcal{E}_2}{\mathcal{N }(\gamma+1)}\frac{1}{\overline{\gamma}}\text{e}^{-\gamma/\overline{\gamma}}\,d\gamma
[/tex]

* Since the original expression was named [tex] G^2 [/tex], this should be something like [tex] E[G^2] [/tex] or [tex] \mu_{G^2} [/tex]

* It appears that the variable of integration is [tex] \gamma [/tex] and [tex] \overline \gamma [/tex] refers to the mean of the exponential distribution. With the form of the denominator in the integral, it does appear that either the constant from the initial expression is missing, or that it was incorrectly typed : could it be that the expression was supposed to be

[tex]
G^2 = \frac{\mathcal{E}_2}{\mathcal{N}\alpha + \mathcal{N}} = \frac{\mathcal{E}_2}{\mathcal{N}\left( \alpha + 1 \right)} \, \text{\huge{?}}
[/tex]

If so, that explains the form of the integral.
 
  • #4
statdad said:
Here

[tex]
G^2=\int_0^{\infty}\frac{\mathcal{E}_2}{\mathcal{N }(\gamma+1)}\frac{1}{\overline{\gamma}}\text{e}^{-\gamma/\overline{\gamma}}\,d\gamma
[/tex]

* Since the original expression was named [tex] G^2 [/tex], this should be something like [tex] E[G^2] [/tex] or [tex] \mu_{G^2} [/tex]

* It appears that the variable of integration is [tex] \gamma [/tex] and [tex] \overline \gamma [/tex] refers to the mean of the exponential distribution. With the form of the denominator in the integral, it does appear that either the constant from the initial expression is missing, or that it was incorrectly typed : could it be that the expression was supposed to be

[tex]
G^2 = \frac{\mathcal{E}_2}{\mathcal{N}\alpha + \mathcal{N}} = \frac{\mathcal{E}_2}{\mathcal{N}\left( \alpha + 1 \right)} \, \text{\huge{?}}
[/tex]

If so, that explains the form of the integral.

I am sorry, I was wrong, it says that:

[tex]G^2=E_{\alpha}\left[\frac{\mathcal{E}_2}{\mathcal{E}_1\,\alpha+\mathcal{N}}\right][/tex]

where [tex]E_X[.][/tex] is the expectation operator. Anyway, the authors has the aforementioned integral evaluated as:

[tex]G^2=\frac{\mathcal{E}_2}{\mathcal{E}_1\,\Omega_1}\,\text{e}^{1/\overline{\gamma}}\,E_1\left(\frac{1}{\overline{\gamma}}\right)[/tex]

where [tex]\overline{\gamma}=\frac{\Omega_1\,\mathcal{E}_1}{\mathcal{N}}[/tex], and [tex]E_1(.)[/tex] is the exponential integral. The final result is consistent with the integration, so, this removes any doubt about typos, doesn't it?
 
  • #5
So, your post was the typo? doesn't matter, as long as the confusion is cleared up for you. glad you got to the bottom of it.
 
  • #6
statdad said:
So, your post was the typo? doesn't matter, as long as the confusion is cleared up for you. glad you got to the bottom of it.

No, I posted what they wrote, except the expectation operator that I forgot. What I meant is that it is unlikely that the authors continue on typos. So, the matter is still stucked for me. I don't know what is the missing part in their solution.
 

FAQ: Can the Expected Value be Written as an Integral?

1. What is Expected Value Evaluation?

Expected Value Evaluation is a statistical method used to calculate the potential outcome of a situation by taking into account all possible outcomes and their probabilities.

2. How is Expected Value Evaluation used in decision making?

Expected Value Evaluation is used in decision making to determine the best course of action by comparing the potential outcomes and their probabilities. It helps in making informed and rational decisions by considering all possible outcomes.

3. What are the key components of Expected Value Evaluation?

The key components of Expected Value Evaluation are the potential outcomes, their probabilities, and the value associated with each outcome. These factors are used to calculate the expected value, which is the sum of each outcome multiplied by its probability.

4. What are the limitations of Expected Value Evaluation?

Expected Value Evaluation is based on assumptions and probabilities, which can sometimes be inaccurate. It also does not take into account any qualitative factors or personal preferences. It is important to consider these limitations when using this method for decision making.

5. How can Expected Value Evaluation be applied in real-world scenarios?

Expected Value Evaluation can be applied in various real-world scenarios, such as in finance to evaluate investment opportunities, in business to make strategic decisions, and in healthcare to determine the best treatment option. It can also be used in risk analysis to assess potential losses and make risk management decisions.

Similar threads

Replies
1
Views
575
Replies
5
Views
644
Replies
2
Views
1K
Replies
16
Views
2K
Replies
12
Views
2K
Back
Top