In the model comparison section of the [hierarchical analysis example](https://cwinpy.readthedocs.io/en/latest/hierarchical.html#model-comparison), I suggest a `HalfGaussian` prior on the `sigma0` value. As `sigma0` is a scale parameter a more sensible prior would be a `LogUniform`, so I should suggest that. A couple of related things: * if running with the numerical likelihood, when using many pulsars then number of grid points at which the individual likelihoods are evaluated becomes important (particularly for when there are many pulsars with posterior support that ranges over a few orders of magnitude). So, I should add information about increasing the number of interpolation bins in these cases, ~or perhaps add the ability to have the bins on a logarithmic scale~ (it seems that past me was ahead of the game and this is already on option using the `gridtype` argument). * ~When storing the interpolated likelihoods as `splrep` outputs, the knots are the same for each case, but I store all of them. The amount of memory could be reduced by only storing the knots once.~ Actually, it is probably worth changing things so that the individual interpolated likelihoods are not all calculated on the same grid, but on individual grids that are specific to the scale of the actual posterior bulk. For inference with many pulsars, having a single grid (as described in the first bullet) can result in extremely spiking likelihoods, which having individual grids may alleviate.