Gradient-based traversal through parameter space.

This implementation of HMC algorithm uses `lens`

as a means to operate over
generic indexed traversable functors, so you can expect it to work if your
target function takes a list, vector, map, sequence, etc. as its argument.

If you don't want to calculate your gradients by hand you can use the
handy ad library for automatic
differentiation.

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.

```
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
```