Denoting random variable $\theta$ with capital $\Theta$? It is common practice to denote random variables with a capital letter $X$ and the realization with a small $x$. But how about in Bayesian statistics? The parameter $\theta$ is a RV, so shouldn't it be written as $\Theta$ and only its realization as $\theta$? 
This is quite confusing for me and no book or whatsoever has ever written a capital $\Theta$ for the parameter... So I'm wondering if I am wrong
 A: There are many conventions, and different people, or institutions, may have different typographical preferences. It is a popular convention to see the data random variables as capitalized Latin letters (e.g. $X$, $Y$) and parameter random variables as lowercase Greek letters (e.g. $\theta$, $\mu$, $\beta$, $\sigma$). The uppercase Greek letters seem to be used more commonly for things like random matrices (e.g. covariance matrix $\Sigma$). Moreover, it is common to see vectors and matrices of random variables in bold font (e.g. $\mathbf{X} = (X_1, X_2, \dots, X_n)'$, or $\boldsymbol{\mu} = (\mu_1, \mu_2, \dots, \mu_k)'$).
Check also other questions tagged as notation, like this thread Should random vectors be Capital? .
A: The usual convention is for parameters to be given lower case Greek letters, and their range is usually denoted with the corresponding upper-case Greek letter (e.g., you have a random variable $\theta$ with range $\Theta$).  You will occasionally run across the use of the upper-case Greek letters to refer to random variables, but it is rare.  Also, bear in mind that in Bayesian statistics you generally don't need to capitalise any of your random variables, because in that methodology, anything appearing in the main argument of a distribution is random, and anything appearing as a conditioning variable is known.  For this reason, many Bayesian books and papers are written without capitalising any of the variables.
