Copyright | (c) Dominik Schrempf 2020 |
---|---|
License | GPL-3.0-or-later |
Maintainer | dominik.schrempf@gmail.com |
Stability | unstable |
Portability | portable |
Safe Haskell | None |
Language | Haskell2010 |
Creation date: Tue May 5 20:11:30 2020.
The Metropolis-Hastings-Green (MHG
) algorithm.
For example, see Geyer, C. J., Introduction to Markov chain Monte Carlo, In Handbook of Markov Chain Monte Carlo (pp. 45) (2011). CRC press.
Synopsis
- newtype MHG a = MHG {}
- mhg :: PriorFunction a -> LikelihoodFunction a -> Cycle a -> Monitor a -> a -> GenIO -> IO (MHG a)
- mhgSave :: ToJSON a => AnalysisName -> MHG a -> IO ()
- mhgLoad :: FromJSON a => PriorFunction a -> LikelihoodFunction a -> Cycle a -> Monitor a -> AnalysisName -> IO (MHG a)
- mhgAccept :: Log Double -> GenIO -> IO Bool
Documentation
The MHG algorithm.
Instances
ToJSON a => Algorithm (MHG a) Source # | |
Defined in Mcmc.Algorithm.Metropolis aName :: MHG a -> String Source # aIteration :: MHG a -> Int Source # aIterate :: ParallelizationMode -> MHG a -> IO (MHG a) Source # aAutoTune :: MHG a -> MHG a Source # aResetAcceptance :: MHG a -> MHG a Source # aSummarizeCycle :: MHG a -> ByteString Source # aOpenMonitors :: AnalysisName -> ExecutionMode -> MHG a -> IO (MHG a) Source # aExecuteMonitors :: Verbosity -> UTCTime -> Int -> MHG a -> IO (Maybe ByteString) Source # aStdMonitorHeader :: MHG a -> ByteString Source # |
:: PriorFunction a | |
-> LikelihoodFunction a | |
-> Cycle a | |
-> Monitor a | |
-> a | The initial state in the state space |
-> GenIO | A source of randomness. For reproducible runs, make sure to use generators with the same seed. |
-> IO (MHG a) |
Initialize an MHG algorithm.
mhgLoad :: FromJSON a => PriorFunction a -> LikelihoodFunction a -> Cycle a -> Monitor a -> AnalysisName -> IO (MHG a) Source #
Load an MHG algorithm.
See mcmcContinue
.