Jeffreys prior for continuous uniform distribution A nonnegative random variable $x$ has a continuous uniform distribution in the interval $(0,\theta)$. Therefore, the likelihood is given by:
$f(x|\theta) = \frac{1}{\theta}I(x\leq\theta)$, where $I$ is an indicator function. 
But what is the Jeffreys prior for the parameter $\theta$? In particular, how to handle the indicator function when calculating the Fisher information?
 A: I think the other answer is wrong, so will give a detailed development here. First, let $X_1, \dotsc, X_n$ be iid uniform on the interval $(0,\theta)$. Then the likelihood function can be written as 
$$
   L(\theta)= \theta^{-n} \cdot \mathbb{1}(\theta \ge T)
$$ where $T=\max(X_1, \dotsc, X_n)$ is the sufficient statistic for $\theta$. The log likelihood then can be written 
$$
 l(\theta)=\log L(\theta)= -n \log \theta+ \begin{cases} 0 &,\theta\ge T \\ -\infty &, \theta<T \end{cases}
$$ and its first derivative (where it exists) can be written
$$
   -n/\theta
$$ with expectation equal to $ -n/\theta \not= 0$, so **we cannot calculate the Fisher information via the expectation of the second derivative, since that equality depends on equality to zero above.  If we nevertheless do that, we end up with Fisher information as $-n/\theta^2$, which is negative so of course impossible. 
Then, using directly the definition of Fisher information, see Wikipedia: Fisher information, we get
$$\DeclareMathOperator{\E}{\mathbb{E}}  
   I(\theta)=\E_{\theta}\left\{ \left[\frac{\partial}{\partial\theta}\log f(x;\theta)\right]^2\right\} = \int_0^\theta [-n/\theta]^2 (1/\theta)\; dx = (n/\theta)^2
$$
Then the Jeffrey's uninformative prior is proportional to its squareroot, that is,
$$
  \pi(\theta) \propto 1/\theta, \quad \theta>0
$$
which is an improper prior. But when $n\ge 2$ we get a proper posterior, which is Pareto with parameters of scale $x_m = L$ and shape $\alpha = n$. The density is given by 
$$
   \pi(\theta \,|\, T) = \frac{n}{\theta} \left( \frac{T}{\theta}\right)^{n}
$$ for $\theta \ge T$ and $n\ge 2$.
Note that the Jeffrey's prior can be seen as a degenerate Pareto conjugate prior with parameters $x_m = 0, \, \alpha = 0$.
A: The Jeffreys prior for $\theta$ doesn't depend upon the indicator function, although of course the posterior will.  The square root of the second derivative of the log likelihood function is all you need: 
$p(\theta) = \left(-\frac{\text{d}^2(\log \theta)}{\text{d}\theta^2}\right)^{1/2}$
When moving on to the posterior, you'll have to remember that indicator function; if $x \leq \theta$ for all $x$, the data says something important about the values that $\theta$ can take.  But it's perfectly OK to have a prior that covers a range of values part of which is ruled out once you observe the data.
