Beta prior on (a,b) Is it possible to rescale a beta prior to range from, say, $(0.5,1.0)$? For example, say that your likelihood function for some parameter $p$ is binomial and you know that $p \in (0.5,1.0)$ due to some physical constraint, is there a conjugate prior for this model?
 A: In general the support space of the prior-if a subset of the likelihood parameter space-will be the support space of the posterior. For this case write the likelihood
$$L(x| p)=  {n\choose{x} }p^{x}(1-p)^{n-x} $$
and the prior as a beta
$$\pi(p| \alpha, \beta) =\frac{\Gamma(\alpha+\beta)p^{\alpha-1}(1-p)^{\beta-1} }{\Gamma(\alpha)\Gamma(\beta)}\mathbb{1}(p\in (a,b))$$
where $\mathbb{1}(A)$ is an indicator function in the set ($A$) and $0\leq a < b \leq 1$.
Now we can drop the irrelevant normalizing constants to see that the posterior will be
$$\pi(p | x, \alpha, \beta) = L(x| p)\times\pi(p| \alpha, \beta) \propto p^{x}(1-p)^{n-x} \times p^{\alpha-1}(1-p)^{\beta-1}\mathbb{1}(p\in (a,b))
\propto p^{\alpha+x-1}(1-p)^{n-x+\beta-1}\mathbb{1}(p\in (a,b)).
$$
Now we can calculate the normalizing constant-at least numerically-by using incomplete beta function
$$C = \left(\int_a^b p^{\alpha+x-1}(1-p)^{n-x+\beta-1}dp\right)^{-1}.$$
So the posterior will be 
$$\pi(p | x, \alpha, \beta)=C\times p^{\alpha+x-1}(1-p)^{n-x+\beta-1}\mathbb{1}(p\in (a,b))$$ 
In the question asked here in the post $a=0.5$ and $b=1.0$.
