Exponential Distribution - Rate - Bayesian Prior? I have gone through WinBugs documentation (for example, http://www.mrc-bsu.cam.ac.uk/bugs/thebugsbook/examples/html/Chapter-11-Specialised/Example-11_7_2-leukaemia.html). And also through this book (http://www.amazon.ca/Bayesian-Survival-Analysis-Joseph-Ibrahim/dp/0387952772).
Both use a gamma distribution prior for the rate (lambda) for the exponential distribution. They alternate between Gamma(0.01,0.01) and Gamma(0.001,0.001). 
I would like to use a non-informative prior.... but I don't think this is it?
Could someone explain whether this is a non-informative prior? If not, can anyone suggest one?
 A: Edit: This answer seems to entail some confusion about different ways to parameterize the Gamma distribution.  It's probably best to ignore it.

I think I know what's going on. It has to do with a decision about
  what you want your prior to be uninformative about: the rate parameter or the distribution of survival times.
Gamma(0.001, 0.001) has a lot of very small values (close to 0).
When the Exponential distribution's rate parameter is close to zero
  ($\epsilon$), then it has a very high expected value (1/$\epsilon$)
  and is very flat over a wide range of values.
In R, you can see this by plotting an exponential distribution with
  mean .0001 from 0 to 100:
curve(dexp(x, .0001), ylim = c(0, 1E-4), to = 100)
It's essentially uniform (i.e. uninformative) over this range.  It's
  much less flat if you look all the way out to 10000, though, which is
  why you might prefer an even smaller rate parameter.
Hope this makes sense.

A: What does uninformative prior mean to you?
If you mean the Jeffreys prior, then it is $\beta \sim \textrm{Gamma}(0,0)$ as @Daniel points out.
If you mean a flat prior (which isn't uninformative, although it gives the illusion of being uninformative), then it is simply $\beta \sim \textrm{Gamma}(1,0)$, which you can verify by looking at the pdf to be an improper flat prior.
However, if there is a big difference between the priors, then you probably don't have enough data.
A: Using $\text{Gamma}(a,b), \; a\approx b \approx 0 $ is uniform logarithmic-ally. This gives the non-informative prior, in terms of what your model want to do with the input data. 
As an example of this, you can see this: 
http://jmlr.csail.mit.edu/papers/volume1/tipping01a/tipping01a.pdf
search for "gamma". 
Also check this out: http://www.stats.org.uk/priors/noninformative/YangBerger1998.pdf 
