Speed up core.prior classes

Extended from #411 (closed)

1. • Profile all of the priors which use scipy: LogNormal, Exponential, StudentT, Logistic, Cauchy, Gamma.
2. • See if we can make them run faster by using analytical solutions in the rescale, prob, logprob, cdf methods.
     • Uniform: merged and dealt with in #411 (closed). O(1)-O(2) faster.
     • LogNormal: 8x to 20x faster.
     • Exponential: 5x to 50x faster.
     • StudentT: 3x to 40x faster.
     • Logistic: 5x to 60x faster.
     • Cauchy: 2x to 500x faster.
     • Gamma: 2x to 600x faster.
     • Beta: 2x to 300x faster.
3. • Add unit tests to ensure that calculations are correct.

(Biggest speed-up is when inputting (float, int), smaller speed-up is inputting np.ndarray)