Speedy, gradient-based traversal through parameter space.
Exports a mcmc function that prints a trace to stdout, a chain function for
collecting results in memory, and a hamiltonian transition operator that can
be used more generally.
If you don't want to calculate your gradients by hand you can use the handy ad library for automatic differentiation.
import Numeric.AD (grad)
import Numeric.MCMC.Hamiltonian
target :: RealFloat a => [a] -> a
target [x0, x1] = negate ((x0 + 2 * x1 - 7) ^ 2 + (2 * x0 + x1 - 5) ^ 2)
gTarget :: [Double] -> [Double]
gTarget = grad target
booth :: Target [Double]
booth = Target target (Just gTarget)
main :: IO ()
main = withSystemRandom . asGenIO $ mcmc 10000 0.05 20 [0, 0] booth
hasty-hamiltonian is a member of the declarative suite of libraries, containing a bunch of MCMC algorithms that play nicely together.