Better samplers for prior parameters
BaxterEaves opened this issue · comments
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?