| Copyright | (c) 2015 Jared Tobin |
|---|---|
| License | MIT |
| Maintainer | Jared Tobin <jared@jtobin.ca> |
| Stability | unstable |
| Portability | ghc |
| Safe Haskell | None |
| Language | Haskell2010 |
Numeric.MCMC.Hamiltonian
Contents
Description
This implementation performs Hamiltonian Monte Carlo using an identity mass matrix.
The mcmc function streams a trace to stdout to be processed elsewhere,
while the slice transition can be used for more flexible purposes, such as
working with samples in memory.
See Neal, 2012 for the definitive reference of the algorithm.
- mcmc :: (MonadIO m, PrimMonad m, Num (IxValue (t Double)), Show (t Double), Traversable t, FunctorWithIndex (Index (t Double)) t, Ixed (t Double), IxValue (t Double) ~ Double) => Int -> Double -> Int -> t Double -> Target (t Double) -> Gen (PrimState m) -> m ()
- chain :: (PrimMonad m, Traversable f, FunctorWithIndex (Index (f Double)) f, Ixed (f Double), IxValue (f Double) ~ Double) => Int -> Double -> Int -> f Double -> Target (f Double) -> Gen (PrimState m) -> m [Chain (f Double) b]
- hamiltonian :: (Num (IxValue (t Double)), Traversable t, FunctorWithIndex (Index (t Double)) t, Ixed (t Double), PrimMonad m, IxValue (t Double) ~ Double) => Double -> Int -> Transition m (Chain (t Double) b)
- data Target a :: * -> * = Target {}
- create :: PrimMonad m => m (Gen (PrimState m))
- createSystemRandom :: IO GenIO
- withSystemRandom :: PrimBase m => (Gen (PrimState m) -> m a) -> IO a
- asGenIO :: (GenIO -> IO a) -> GenIO -> IO a
Documentation
mcmc :: (MonadIO m, PrimMonad m, Num (IxValue (t Double)), Show (t Double), Traversable t, FunctorWithIndex (Index (t Double)) t, Ixed (t Double), IxValue (t Double) ~ Double) => Int -> Double -> Int -> t Double -> Target (t Double) -> Gen (PrimState m) -> m () #
Trace n iterations of a Markov chain and stream them to stdout.
>>>withSystemRandom . asGenIO $ mcmc 10000 0.05 20 [0, 0] target
chain :: (PrimMonad m, Traversable f, FunctorWithIndex (Index (f Double)) f, Ixed (f Double), IxValue (f Double) ~ Double) => Int -> Double -> Int -> f Double -> Target (f Double) -> Gen (PrimState m) -> m [Chain (f Double) b] #
Trace n iterations of a Markov chain and collect the results in a list.
>>>results <- withSystemRandom . asGenIO $ chain 1000 0.05 20 [0, 0] target
hamiltonian :: (Num (IxValue (t Double)), Traversable t, FunctorWithIndex (Index (t Double)) t, Ixed (t Double), PrimMonad m, IxValue (t Double) ~ Double) => Double -> Int -> Transition m (Chain (t Double) b) #
A Hamiltonian transition operator.
Re-exported
A Target consists of a function from parameter space to the reals, as
well as possibly a gradient.
Most implementations assume a log-target, so records are named accordingly.
createSystemRandom :: IO GenIO #
Seed a PRNG with data from the system's fast source of pseudo-random
numbers. All the caveats of withSystemRandom apply here as well.
withSystemRandom :: PrimBase m => (Gen (PrimState m) -> m a) -> IO a #
Seed a PRNG with data from the system's fast source of
pseudo-random numbers ("/dev/urandom" on Unix-like systems or
RtlGenRandom on Windows), then run the given action.
This is a somewhat expensive function, and is intended to be called
only occasionally (e.g. once per thread). You should use the Gen
it creates to generate many random numbers.