Conjugate normal distributions for random mean and (co)variance and posterior updating.

I've implemented the basics of the conjugate distributions for a (multivariate) Normal with unknown mean and (co)variance.  I've also made a `posterior_sample` function that given a prior, observation model, and data, returns a new distribution with the same type as the observation model but with parameter sampled from the posterior.  This is useful for Gibbs sampling.

The interfaces for the distributions can be filled out as necessary and the tests should be expanded accordingly.  Additionally, the tests should probably test the correctness of the posteriors.  It's on the todo list, but I wanted people to mull this over a bit.

In the future it would be good to implement versions of these for multivariate Normal distributions with isotropic and diagonal covariances (this will require modified sufficient statistics).

Also, the `MvNormalStats` type could be used to compute MLE and MAP estimates.

Lastly, I moved `lpgamma` to specialfuns.jl from wishart.jl as it's useful in other places now.