This seems to be dependant on the type of generator you use. Try using lagged_fibonacci. The casting and dividing involved in uniform_real appears to loose a lot of accuracy, but lagged_fibonacci generates doubles between 0 and 1, so there's no cast and it's dividing by 1.
Good idea.
Interestingly, minstd_rand also seems to work better. Purely speculatively, I think this might be because of the ranges of values it generates - with mt19937 the random number is divided by 0xffffffff, while for minstd_rand it is divided by 0x7ffffffd. Although, I wouldn't be surprised if it performs badly by some other measure.
Come to think of it, uniform_real is meant to be generating a half-open range - so maybe it should be diving by (max() - min() + 1) for integers. Looking at the concept documentation: 'For integer generators (i.e. integer T), the generated values x fulfill min() <= x <= max(), for non-integer generators (i.e. non-integer T), the generated values x fulfill min() <= x < max().'
Or are you only meant to use floaing point generators when generating a floating point distribution?
No, but.... I've had a private mail from Jens to indicate that this is by design, or at least it's a QOI issue not a bug. In the current Boost implementation if you use a generator that produces int's, then you only get sizeof(int)*CHAR_BIT random bits in the result (which may or may not get distributed throughout the double). One solution seems to be to pack a couple of random int's into one double, but it's a hack I'd rather not get into just now. I'll try again with a lagged_fibonacci and see how it looks... Thanks all, John.