Conditional density function - please

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Discussion Overview

The discussion revolves around finding the conditional density function of a signal transmitted through an additive Gaussian noise channel. Participants explore the mathematical formulation of the conditional density function given the signal and noise characteristics, including the use of Bayes' theorem and convolution of probability distributions.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant presents the problem of finding the conditional density function fx(x|y) using Bayes' theorem, noting the independence of the signal and noise.
  • Another suggests using Fourier transforms and the convolution theorem as a potential method to approach the problem.
  • Clarification is sought regarding the definition of the output y, with some participants agreeing that y is the sum of the noise and the signal.
  • One participant provides the specific forms of the signal and noise distributions, indicating that the signal is an exponential function and the noise is Gaussian.
  • Another participant mentions that the convolution of an exponential and a normal distribution can be approximated by another exponential distribution.
  • A participant expresses difficulty in obtaining a valid probability density function for certain values of y after plotting the results, questioning the implications of this outcome.
  • There is uncertainty about the interpretation of "impossible" in relation to determining x for specific cases of y.

Areas of Agreement / Disagreement

Participants express differing views on the interpretation of the output and the methods to find the conditional density function. There is no consensus on the best approach or the implications of the plotted results.

Contextual Notes

Participants have not resolved the mathematical steps involved in convoluting the distributions or the implications of the results obtained from plotting the conditional density function.

ionlylooklazy
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conditional density function - need help please!

given

a signal x, is a random variable which is expontential with a mean of 3. it is transmitted through an additive gaussian noise channel, where the gaussian noise has a mean of -2 and a variance of 3. the signal and noise are independent.


Find an expression for the CDF (conditional density function) of the signal given the observation of the output. fx(x|y)

what i think...

from bayes theorem i know:

fx(x|y) = fx(y|x)*fx(x) / fy(y)

assuming:
output = y
noise = n
input = x

y = n+x

how do i find fx(y|x) ?

the only info i have are the probability density function's for x and n

also every attempt at convoluting the exponential with the guassian (to find y) has failed whether by hand, calculator, or matlab
 
Last edited:
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Since I'm headed to bed right now - I don't have time to think about this more thoroughly, but maybe you could work with the Fourier transforms and take advantage of the convolution theorem?
 
Whats y? n + x?
 
i assume y is is n + x, that is all the information given (the top paragraph) and the question to find the expression for CDF fx(x|y), so y should be the convolution of n and x
 
Last edited:
y is the output however you are not interpreting the signals i think?

The given signal is 3e^(-3x) and the noise is Gaussian(-2,3)
The output is additive which means,
y = 3e^(-3x) + Gaussian(-2,3)
Now can u find f(y|x) ?

-- AI
 
Some follow ups:
The convolution of an exponential and a and a normal distribution is approximated by another exponential distribution.

http://rkb.home.cern.ch/rkb/AN16pp/node38.html

Also - the conditional pdf f(y|x) would, intuitively to me, be a Gaussian with mean (x-2).
 
Last edited by a moderator:
ah of course, many thanks


also, after i found everything i simplified fx(x|y) and plotted it for the cases y = {-5 -1 0 1 5 10} but only the cases y = {0 1} turned out something resembling a probability density function, would this just be that it is impossible to determine x for these cases?
 
Last edited:
Not sure what you mean by impossible.
 

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