This package presents a simple combinator language for Markov transition
operators that are useful in MCMC.
Any transition operators sharing the same stationary distribution and obeying
the Markov and reversibility properties can be combined in a couple of ways,
such that the resulting operator preserves the stationary distribution and
desirable properties amenable for MCMC.
We can deterministically concatenate operators end-to-end, or sample from
a collection of them according to some probability distribution. See
Geyer, 2005 for details.
A useful strategy is to hedge one's 'sampling risk' by occasionally
interleaving a computationally-expensive transition (such as a gradient-based
algorithm like Hamiltonian Monte Carlo or NUTS) with cheap Metropolis
transitions.
transition = frequency [
(9, metropolis 1.0)
, (1, hamiltonian 0.05 20)
]
Alternatively: sample consecutively using the same algorithm, but over a
range of different proposal distributions.
transition = concatAllT [
slice 0.5
, slice 1.0
, slice 2.0
]
Or just mix and match and see what happens!
transition =
sampleT
(sampleT (metropolis 0.5) (slice 0.1))
(sampleT (hamiltonian 0.01 20) (metropolis 2.0))
Check the test suite for example usage.
