declarative

DIY Markov Chains.

Build composite Markov transition operators from existing ones for fun and profit.

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.