Better samplers for prior parameters

[BaxterEaves]

Prior parameter samples are rejected too much.

The following MH samplers for prior parameters:

• Symmetric random walk, which rejects too much
• Prior MH (the hyper prior is the proposal), which rejects too much but is really fast at rejecting least
• Globally adaptive MCMC, which is finicky for unknown reasons

We also have:

• Slice sampler, which uses a "stepping out" procedure to find the slice region, but which can step out forever (causing slowness and eventually a panic) under certain circumstances.
• Importance MH, which is like prior MH, but uses a user-defined function

Better options:

• HMC
• PDMDP
• Other adaptive MH schemes?