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I would like to Change $\sigma$ to $\log \sigma$ in Normal distribution, $$ P(x) = \frac{1}{{\sigma \sqrt {2\pi } }} \exp \left\{{{{ - \left( {x - \mu } \right)^2 } \mathord{\left/ {\vphantom {{ - \left( {x - \mu } \right)^2 } {2\sigma ^2 }}} \right.} {2\sigma ^2 }}} \right\}. $$

I know so far the transformation of random variables such as $f_{X,Y}$ to $f_{U,V}$ where $X\sim {\rm beta}(\alpha, \beta), \ Y\sim {\rm beta}(\alpha + \beta, \gamma)$, $U=XY$ and $V=X$.

But if I want to transform the parameter, such as $\sigma$ above, how should I deal with it?

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You can just replace $\sigma$ in

$$ P(x) = \frac{1}{{\sigma \sqrt {2\pi } }} \exp \left\{{{{ - \left( {x - \mu } \right)^2 } \mathord{\left/ {\vphantom {{ - \left( {x - \mu } \right)^2 } {2\sigma ^2 }}} \right.} {2\sigma ^2 }}} \right\}$$

with $\exp{\ln{\sigma}}$, thereby yielding the following

$$ P(x) = \frac{1}{{\exp{\ln{\sigma}} \sqrt {2\pi } }} \exp \left\{{{{ - \left( {x - \mu } \right)^2 } \mathord{\left/ {\vphantom {{ - \left( {x - \mu } \right)^2 } {2\sigma ^2 }}} \right.} {2(\exp{\ln{\sigma}})^2 }}} \right\}$$

which can be further rewritten as follows

$$ P(x) = \frac{1}{{\sqrt {2\pi } }} \exp \left\{{{{ - \left( {x - \mu } \right)^2 } \ln{\sigma}\mathord{\left/ {\vphantom {{ - \left( {x - \mu } \right)^2 } {2\sigma ^2 }}} \right.} {2\exp{(2 \ln{\sigma})} }}} \right\}.$$

Since $\sigma$ here is a fixed parameter rather than a random variable, no further details are necessary. However, should $\sigma$ be a random variable with probability $P(\sigma)$, and should you want to use $\ln \sigma$ there, namely $P(\ln \sigma)$, then you should change $P(\sigma)$ accordingly.

Is that what you want? Does this helps? If not, could you reformulate the question? Thanks.

UPDATE:

It seems to me that what they are doing in that post is analogous to the following $$\int{f(x) P(x|\sigma) P(\sigma) d\sigma dx}$$ and that they tried to compute this but directly replacing $\sigma$ with $\ln{\sigma}$. In order to obtain the same results, the integral must be expressed as $$\int{f(x) P(x|\ln{\sigma}) P(\ln{\sigma}) \frac{1}{\sigma} d\sigma dx} $$, that is, by taking into account that changing $\sigma$ into $\ln\sigma$ also changes the differential within the integral. The result would be perhaps clearer is we write $\sigma=\ln{u}$. In that case, the integral would read $$\int{f(x) P(x|\ln{u}) P(\ln{u}) \frac{1}{u} du dx}. $$ I may be wrong about what they tried to do in that post, but this may hopefully help you anyway.

  • 0
    If you multiply $X$ by $\frac{\log(\sigma)}{\sigma}$ then the new variance will be $\log(\sigma)^2$. However $\mu$ becomes $\frac{\log(\sigma)}{\sigma}\mu$. Is the the latter fact the one that disturbs you?2017-02-14
  • 0
    I was looking at [this post](http://stats.stackexchange.com/questions/261768/make-a-standard-deviation-always-positive-in-metropolis-hastings/261786#comment500945_261768) which says we need to consider the Jacobian of the transformation. That's why I'm confused.2017-02-14