Suppose and are independently distributed exponential random variables with mean 1. This means . What is the distribution of ? Here is how one might proceed. Firstly note that must be between 0 and 1.
This shows that is uniformly distributed between 0 and 1, a beautiful result!
if are iid exponential, then for the ratio has the same distribution as the ‘th smallest number out of numbers chosen independently and uniformly between 0 and 1.
This is also known as the Beta distribution with parameters k, n-k. We consider a Poisson process where the time between arrivals is exponentially distributed. The ratio represents the proportion of waiting time for the k’th arrival to the n’th arrival.
The proof of this result can be seen for example in  or , but we will use another approach that I learnt as a student – one using counting processes.
Denote the partial sum by , the time to the k’th arrival. We wish to show that given the joint distribution of is the same as the joint distribution of , where is the ‘th smallest sample of chosen iid uniformly in . Dividing by and then taking expectations over the distribution of would then lead to the result.
Define the counting process (the number of arrivals in time s) by
By the definition of and this is a Poisson process. For We can decompose into the sum of independent Poisson random variables , . Given they have a multinomial distribution with trials and probabilities .
Next define the corresponding process determined by uniform random variables :
This can also be written as the sum of indicator variables where
Each indicator variable is a Bernoulli random variable with probability . One may decompose , have multinomial distribution with probabilities .
This is the same distribution as that found above for given . We conclude that the counting processes and have the same finite-dimensional distributions (given ), so and from which they are defined do too. This completes the proof.
By the way, if and were independent and uniformly distributed between 0 and 1, then what is the distribution of in this case? We have for and for giving us for ,
For this becomes
For this is
By taking derivatives we find that the pdf (probability density function) of is given by
The cdf and pdf of this random variable are plotted below.
Hence we see that when two numbers are chosen uniformly between 0 and 1, the ratio of one of them to their sum is more likely to be close to 0.5 than to any other value. When the two numbers are chosen according to an exponential distribution, the same ratio is uniform between 0 and 1.