stochaskell-1.0.0: A probabilistic programming language

Copyright	(c) David A Roberts 2015-2021
License	GPL-3
Maintainer	d@vidr.cc
Stability	experimental
Safe Haskell	None
Language	Haskell2010

Language.PyMC3

Description

Synopsis

data PyMC3Inference = PyMC3Sample {
- pmDraws :: Int
- pmStep :: Maybe PyMC3Step
- pmInit :: Maybe String
- pmStart :: Env
- pmTune :: Int
- pmChains :: Maybe Int
- pmFloatX :: String
}
data PyMC3Step
- = NUTS
- | Metropolis
- | Slice
- | HamiltonianMC {
  - pathLength :: Double
  - stepRand :: String
  - stepScale :: Double
  }
defaultPyMC3Inference :: PyMC3Inference
runPyMC3 :: (ExprTuple t, Read t) => PyMC3Inference -> P t -> Maybe t -> IO [t]
pmProgram :: ExprTuple t => P t -> String -> String
pmProgram' :: ExprTuple t => PyMC3Inference -> P t -> Maybe t -> String

Documentation

data PyMC3Inference #

Constructors

PyMC3Sample
Fields pmDraws :: Int pmStep :: Maybe PyMC3Step pmInit :: Maybe String pmStart :: Env pmTune :: Int pmChains :: Maybe Int pmFloatX :: String

Instances

Show PyMC3Inference #
Instance details Defined in Language.PyMC3 Methods showsPrec :: Int -> PyMC3Inference -> ShowS # show :: PyMC3Inference -> String # showList :: [PyMC3Inference] -> ShowS #

data PyMC3Step #

Constructors

NUTS
Metropolis
Slice
HamiltonianMC
Fields pathLength :: Double stepRand :: String stepScale :: Double

Instances

Show PyMC3Step #
Instance details Defined in Language.PyMC3 Methods showsPrec :: Int -> PyMC3Step -> ShowS # show :: PyMC3Step -> String # showList :: [PyMC3Step] -> ShowS #

defaultPyMC3Inference :: PyMC3Inference #

default PyMC3 configuration, see https://docs.pymc.io/api/inference.html

runPyMC3 :: (ExprTuple t, Read t) => PyMC3Inference -> P t -> Maybe t -> IO [t] #

perform inference via the PyMC3 code generation backend

pmProgram :: ExprTuple t => P t -> String -> String #

generate PyMC3 code, as used by runPyMC3

pmProgram' :: ExprTuple t => PyMC3Inference -> P t -> Maybe t -> String #