Conditional probability for normally R.V

In summary: So, in summary, the conversation is about calculating the probability of correct detection in a frequency modulation setup where the signal is divided into 5 slots and 2 "holes" represent a binary message. The noise in slots 1 and 2 must be smaller than the noise + mu in order for a correct detection. The probability for correct detection can be calculated using either Eq. 3 or Eq. 4, where the transition from Eq. 1 to Eq. 2 is legal and the independence of slots 1, 2, 3, 4, and 5 can be assumed. The theory of "order statistics" can be used to find out how the max and min are distributed.
  • #1
ehudwe
4
0
kGRAN.png


w1,w2,w3,w4,w5 are normally distributed i.i.d R.V.

I want to calculate the probability that slots 1&2(w1,w2) are smaller than slots 3,4,5 (where mu>0)

I'm able to calculate the probability that slot 1 is smaller than 3,4,5, now I'm stuck on the calculation for slot 2, where i know that i have to consider the outcomes from the first calculation, for example if i know that w1(slot 1) is larger than w2(slot 2), than the probability that w2(slot 2) is smaller than (3,4,5) is 1.

until now i was sure that the whole probability is :

$$
P_{correct}= P(w_1<\mu+w_3)P(w_1<\mu+w_4)P(w_1<\mu+w_5)P(w_2<\mu+w_3)P(w_2<\mu+w_4)P(w_2<\mu+w_5)
$$
now i am thinking of:
$$
P_{correct}= P(w_1<\mu+w_3)P(w_1<\mu+w_4)P(w_1<\mu+w_5)P(w_2<\mu+w_3|w_1<w_2)P(w_2<\mu+w_4|w_1<w_2)P(w_2<\mu+w_5|w_1<w_2)P(w_2<\mu+w_3|w_2<w_1)P(w_2<\mu+w_4|w_2<w_1)P(w_2<\mu+w_5|w_2<w_1)
$$where the last 3 terms are equal to 1 because i have the knowledge that $$P(w1<μ+w3)P(w1<μ+w4)P(w1<μ+w5) $$
any help will be appreciated.
 
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  • #2
How do i edit my post ? i want to revise the question
 
  • #3
rewrite my question, i didn't find how to just edit

the following modulation is in the frequency domain, x-axis is frequency , y-axis is amplitude of electric current.

the signal is a pulse divided to 5 equally slots by means of electrical current where 2 "holes" indicating the binary message.
for this example my constellation size is 2^3. therefore 5C2 = 10 combinations is possible in this setup (5 slots and 2 "holes") and i'll will use 8 of them.

i am trying to calculate probability as described :


kGRAN.png


w1,w2,w3,w4,w5 are normally distributed i.i.d R.V (AWGN).

A correct detection is when the noise in slots 1 and 2 both is smaller than the noise + mu(the current related to the slot).

$$
w_1,w_2<min(\mu+w_3,\mu+w_4,\mu+w_5)
$$


so the probability for correct detection as I'm thinking is:

$$
1. P_{correct}= P(w_1<\mu+w_3\cap w_1<\mu+w_4\cap w_1<\mu+w_5\cap w_2<\mu+w_3\cap w_2<\mu+w_4\cap w_2<\mu+w_5)
$$

if I'm right, i can calculate just one of the terms , because of the i.i.d properties.
$$
2. P_{correct}= P(w_1<\mu+w_3)P(w_1<\mu+w_4)P(w_1<\mu+w_5)P(w_2<\mu+w_3)P(w_2<\mu+w_4)P( w_2<\mu+w_5)
$$
$$
P_{correct}= P(w_1<\mu+w_3)^6
$$
where
$$
P(w_1<\mu+w_3)= \int_{-\infty}^{\infty}\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{w_1^2}{2\sigma^2}}Q(\frac{w_1-\mu}{\sigma})dw_1
$$
hence the error probability is :
$$
P_{error}=1-P_{correct}
$$
therefore the general case with n- slots and k- "holes" the probability is
$$
3.P_{correct}=\left[\int_{-\infty}^{\infty}\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{x^2}{2\sigma^2}}Q(\frac{x-\mu}{\sigma})dx\right]^{(n-k)k}
$$
i did another calculation with my friend and we got:
$$
4.P_{correct}=\left[\int_{-\infty}^{\infty}\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{x^2}{2\sigma^2}}\left[Q(\frac{x-\mu}{\sigma})\right]^{(n-k)}dx\right]^k
$$

i'm trying to understand which of Eq.3 or Eq.4 is the correct answer ? and the transition from Eq.1 to Eq.2 is legal, meaning, are slots 1,2,3,4,5 are absolutely independent ?
 
  • #4
ehudwe said:
A correct detection is when the noise in slots 1 and 2 both is smaller than the noise + mu(the current related to the slot).

$$
w_1,w_2<min(\mu+w_3,\mu+w_4,\mu+w_5)
$$

A way to put that in familiar territory is:

[itex]max(w_1,w_2) < min(\mu+w_2,\mu+w_4,\mu+w_5) [/itex]

Look at the theory of "order statistics" to find out how the max and min are distributed.
 
  • #5
thanks I'm hoping it will do the work.
 

FAQ: Conditional probability for normally R.V

What is a normally distributed random variable?

A normally distributed random variable is a type of continuous random variable that follows a symmetric bell-shaped curve, also known as a normal distribution. It is characterized by its mean and standard deviation, and many natural phenomena such as human height and blood pressure can be modeled using this distribution.

What is conditional probability?

Conditional probability is the probability of an event occurring given that another event has already occurred. It is denoted by P(A|B), where A is the event of interest and B is the condition. It can be calculated by dividing the probability of both events occurring by the probability of the condition alone.

How is conditional probability calculated for normally distributed random variables?

To calculate conditional probability for normally distributed random variables, we use the formula P(A|B) = P(A∩B) / P(B), where P(A∩B) is the joint probability of A and B occurring and P(B) is the probability of B occurring. We can use the normal distribution function and the given mean and standard deviation to find these probabilities.

What is the relationship between conditional probability and independence?

If two events A and B are independent, then the conditional probability P(A|B) is equal to the marginal probability P(A). This means that the occurrence of event B does not affect the probability of event A occurring. However, if two events are not independent, then the conditional probability will be different from the marginal probability.

What are some real-life applications of conditional probability for normally distributed random variables?

Conditional probability for normally distributed random variables can be applied in various fields such as finance, quality control, and healthcare. For example, it can be used to calculate the probability of stock prices reaching a certain level based on past performance, or to determine the likelihood of a medical test result being accurate given the prevalence of a disease in a population.

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