Verifying Solution for Exponentially Distributed Random Vars.

In summary, we have two i.i.d. random variables $X$ and $Y$ with $X \sim \exp(1)$ and $Y \sim \exp(1)$. We are looking for the probability $\Phi$ which is defined as $\mathbb{P}\left[P_v \geq A + \frac{B}{Y}\right]$. However, the analytical solution does not match with the simulation and we are looking for someone to rectify our mistake. The solution is given by $\Phi = \exp\left(-\frac{D+B}{ab}\left(1 + \frac{e^\varphi}{1+c}\right)\right)\exp\left(-\frac{P_a
  • #1
user_01
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Given two i.i.d. random variables $X,Y$, such that $X\sim \exp(1), Y \sim \exp(1)$. I am looking for the probability $\Phi$. However, the analytical solution that I have got does not match with my simulation. I am presenting it here with the hope that someone with rectifies my mistake.

:


$$\Phi =\mathbb{P}\left[P_v \geq A + \frac{B}{Y}\right] $$

$$
P_v=
\left\{
\begin{array}{ll}
a\left(\frac{b}{1+\exp\left(-\bar \mu\frac{P_s X}{r^\alpha}+\varphi\right)}-1\right), & \text{if}\ \frac{P_s X}{r^\alpha}\geq P_a,\\
0, & \text{otherwise}.
\end{array}
\right.
$$

---
**My solution**

\begin{multline}
\Phi = \mathbb{P}\left[ a\left(\frac{b}{1+\exp\left(-\bar \mu\frac{P_s X}{r^\alpha}+\varphi\right)}-1\right) \geq A + \frac{B}{Y}\right]\mathbb{P}\left[X \geq \frac{P_ar^\alpha}{P_s}\right] + {\mathbb{P}\left[0 \geq A + \frac{B}{Y}\right] \mathbb{P}\left[X \geq \frac{P_ar^\alpha}{P_s}\right]}
\end{multline}

Given that $\mathbb{P}[0>A+\frac{B}{Y}] = 0$

$$ \Phi = \mathbb{P}\left[a\left(\frac{b}{1+\exp\left(-\bar \mu\frac{P_s X}{r^\alpha}+\varphi\right)}-1\right) \geq A + \frac{B}{Y}\right] \exp\left(-\frac{P_ar^\alpha}{P_s}\right) $$

consider $D = A + a$, $c = \frac{\bar{\mu}P_s}{r^{\alpha}}$

$$ \Phi = \mathbb{P} \left[Y \geq \frac{1}{ab}\mathbb{E}_Y[DY + B]. \mathbb{E}_X [1+e^\varphi e^{-c X}] \right] $$

$$\Phi = \exp\left(-\frac{D+B}{ab}\left(1 + \frac{e^\varphi}{1+c}\right)\right)\exp\left(-\frac{P_a r^\alpha}{P_s}\right) $$
 

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  • #2
I did not include the whole intermediate steps in the above solution which may cause confusion. Hence those steps are now presented below.

---
**My solution**

\begin{multline}
\Phi = \mathbb{P}\left[ a\left(\frac{b}{1+\exp\left(-\bar \mu\frac{P_s X}{r^\alpha}+\varphi\right)}-1\right) \geq A + \frac{B}{Y}\right]\mathbb{P}\left[X \geq \frac{P_ar^\alpha}{P_s}\right] + {\mathbb{P}\left[0 \geq A + \frac{B}{Y}\right] \mathbb{P}\left[X \geq \frac{P_ar^\alpha}{P_s}\right]}
\end{multline}

Given that $\mathbb{P}[0>A+\frac{B}{Y}] = 0$

$$ \Phi = \mathbb{P}\left[a\left(\frac{b}{1+\exp\left(-\bar \mu\frac{P_s X}{r^\alpha}+\varphi\right)}-1\right) \geq A + \frac{B}{Y}\right] \exp\left(-\frac{P_ar^\alpha}{P_s}\right) $$

Now consider only the first part of the expression on the right side of the equation, and letting $c = \frac{\bar{\mu}P_s}{r^{\alpha}}$.

$$ \mathbb{P}\left[a\left(\frac{b}{1+\exp\left(-\bar \mu\frac{P_s X}{r^\alpha}+\varphi\right)}-1\right) \geq A + \frac{B}{Y}\right] = \mathbb{P}\left[\frac{ab}{1+\exp\left(-c X+\varphi\right)}-a \geq A + \frac{B}{Y}\right]$$

consider $D = A + a$, and with mathematical manipulations, we get:

$$ \Phi = \mathbb{P} \left[Y \geq \frac{1}{ab}\mathbb{E}_Y[DY + B]. \mathbb{E}_X [1+e^\varphi e^{-c X}] \right] $$

$$\Phi = \exp\left(-\frac{D+B}{ab}\left(1 + \frac{e^\varphi}{1+c}\right)\right)\exp\left(-\frac{P_a r^\alpha}{P_s}\right) $$
 

1. What is an exponentially distributed random variable?

An exponentially distributed random variable is a continuous probability distribution that is commonly used to model the time between events in a Poisson process. It is characterized by a single parameter, λ, which represents the rate at which events occur.

2. How do you verify a solution for an exponentially distributed random variable?

To verify a solution for an exponentially distributed random variable, you can use the cumulative distribution function (CDF). The CDF for an exponentially distributed random variable is F(x) = 1 - e^(-λx). You can plug in the values for λ and x to calculate the probability of a random variable being less than or equal to x.

3. What is the mean of an exponentially distributed random variable?

The mean of an exponentially distributed random variable is equal to 1/λ. This means that on average, the time between events in a Poisson process will be 1/λ units of time.

4. How do you interpret the parameter λ in an exponentially distributed random variable?

The parameter λ represents the rate at which events occur in a Poisson process. This means that for every unit of time, on average, λ events will occur. It also affects the shape of the distribution, with larger values of λ resulting in a steeper curve and smaller values resulting in a flatter curve.

5. Can an exponentially distributed random variable take on negative values?

No, an exponentially distributed random variable cannot take on negative values. It is a continuous distribution that is only defined for positive values. This is because the time between events in a Poisson process cannot be negative.

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