Bayes formula and picking variance from distribution

AI Thread Summary
The discussion revolves around understanding the distribution of a random variable drawn from a normal distribution where its variance is also drawn from another normal distribution. Participants clarify that the joint probability density function can be expressed in terms of conditional distributions, allowing for the calculation of the overall distribution. However, it is noted that the variance of a distribution must be positive, which complicates the assumption of a normal distribution for the variance itself. The conversation highlights the challenges in integrating these distributions, particularly concerning the behavior at the origin. Ultimately, the problem is framed as more of a thought experiment rather than a practical application.
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This should be rather simple bayesian problem, but I can't figure it out for myself.

If i pick numbers from normal distributionS, where the variance of the distribution at each pick v1, is in turn picked out of a normal distribution with variance v2.

What is then the distribution of the random number? i tried this on my calculator a bit, and it looks as if it is normal itself, but what is the variance of x?

This is not homework, but something I would like to understand how to calculate.

//Cal
 
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If i pick numbers from normal distributionS, where the variance of the distribution at each pick v1, is in turn picked out of a normal distribution with variance v2.

You need to clarify this.
 
If you have two continuous random variables (for example), X and Y, with joint pdf \rho_{X,Y}(x,y), this can be written in terms of the conditional distribution \rho_{X|Y}(x|y) or \rho_{Y|X}(y|x) as:

\rho_{X,Y}(x,y) = \rho_{X|Y}(x|y)\rho_Y(y) = \rho_{Y|X}(y|x)\rho_X(x),

where the individual distributions are

\rho_X(x) = \int_{-\infty}^\infty dy~\rho_{X,Y}(x,y),
and similarly for y. So, if you knew the distribution for y and you knew the condition distribution for x given y, you can calculate the distribution of x as

\rho_X(x) = \int_{-\infty}^\infty dy~\rho_{X|Y}(x|y)\rho_Y(y).

Amusingly enough, I was dealing with this concept myself this week, although in the context of a much more intractable problem.

This said, I think you need to tweak your suggested problem. The variance of a distribution is positive, so it can't be normally distributed. If you use the standard deviation, I don't think you'll get a result, because you'll have a factor of 1/\sigma in your integration, why of course blows up at the origin, but the exponential will also be even in sigma, so the principal value might be zero.
 
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Thank you mute, all you said seems very right and true to me. with large enough mu compared to sigma it will become pretty normal. I might have to use some other distribution to pick the variance if i use this for something. But this was more of a though experiment.
 
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