-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/


-- | Parallel code generation for hierarchical Bayesian modeling.
--   
--   Passage is a PArallel SAmpler GEnerator. The user specifies a
--   hierarchical Bayesian model and data using the Passage EDSL, and
--   Passage generates code to sample the posterior distribution in
--   parallel.
--   
--   Currently Passage targets C with OpenMP threads.
@package passage
@version 0.1

module Language.Passage.Distribution
logit :: Floating a => a -> a
logBeta :: Expr -> Expr -> Expr
logFact :: Expr -> Expr
logComb :: Expr -> Expr -> Expr

-- | A normal distribution with mean 0 and precision 1
stdNormal :: Distribution

-- | A normal distribution, with a mean and precision
normal :: Expr -> Expr -> Distribution
standardUniform :: Distribution
uniform :: Expr -> Expr -> Distribution
discreteUniform :: Expr -> Distribution
geometric :: Expr -> Distribution

-- | A categorical distribution with given support size and probabilities |
--   Probabilities are assumed to add to one (not checked here)
categorical :: Expr -> [Expr] -> Distribution

-- | A Bernoulli distribution with a mean
bernoulli :: Expr -> Distribution

-- | A binomial distribution with given number of samples and probability
--   of success | Number of samples is assumed to be fixed
binomial :: Expr -> Expr -> Distribution
negBinomial :: Expr -> Expr -> Distribution
poisson :: Expr -> Distribution

-- | A beta distribution with the given prior sample sizes.
beta :: Expr -> Expr -> Distribution

-- | A gamma distribution with the given prior sample sizes.
dgamma :: Expr -> Expr -> Distribution

-- | An improper uniform distribution; has no impact on likelihood
improperUniform :: Distribution

-- | An improper scale
improperScale :: Distribution

module Language.Passage.UI

-- | A normal distribution, with a mean and precision
normal :: Expr -> Expr -> BayesianNetwork Expr
standardUniform :: BayesianNetwork Expr
uniform :: Expr -> Expr -> BayesianNetwork Expr

-- | A Bernoulli distribution with a mean
bernoulli :: Expr -> BayesianNetwork Expr

-- | A beta distribution with the given prior sample sizes.
beta :: Expr -> Expr -> BayesianNetwork Expr

-- | A gamma distribution with the given prior sample sizes.
dgamma :: Expr -> Expr -> BayesianNetwork Expr

-- | A chi-square distribution with the given degrees of freedom.
chiSquare :: Expr -> BayesianNetwork Expr

-- | An exponential distribution with the given rate (inverse scale)
dexp :: Expr -> BayesianNetwork Expr

-- | A Student's T distribution, given the degrees of freedom.
studentT :: Expr -> BayesianNetwork Expr
symDirichlet :: Int -> Expr -> BayesianNetwork [Expr]

-- | An improper uniform distribution; has no impact on likelihood
improperUniform :: BayesianNetwork Expr
improperScale :: BayesianNetwork Expr

module Language.Passage
data BayesianNetwork a

-- | A Bayesian Network is a collection of stochastic nodes. Stochastic
--   nodes may be (optionally) grouped into arrays.
data BayesianGraph
BayesianGraph :: !IntMap StoVar -> !IntMap ArrayInfo -> BayesianGraph
stoNodes :: BayesianGraph -> !IntMap StoVar
stoArryas :: BayesianGraph -> !IntMap ArrayInfo

-- | A Stochastic variable.
data StoVar
StoVar :: StoVarName -> PriorInfo -> !Map Expr Expr -> StoVar
stoVarName :: StoVar -> StoVarName
stoVarPrior :: StoVar -> PriorInfo

-- | Maps terms that mention the variable to their coefficients, which do
--   not depend on the variable. The term for the distribution is the sum
--   of the products of the map elements (see <tt>stoPostLL</tt>).
stoPostDistLL :: StoVar -> !Map Expr Expr
type Node = Expr
data BayesianSimulator a
logGamma :: Term a -> Term a
using :: Distribution -> BayesianNetwork Expr
tconst :: Double -> Term a

-- | Create a 1D vector
vector :: (Int, Int) -> (Int -> BayesianNetwork Expr) -> BayesianNetwork ([Expr] -> Expr)

-- | Create a 2D matrix
matrix :: (Int, Int) -> (Int, Int) -> ([Int] -> BayesianNetwork Expr) -> BayesianNetwork ([Expr] -> Expr)

-- | Create an &gt;= 3D array
nodeArray :: [(Int, Int)] -> ([Int] -> BayesianNetwork Expr) -> BayesianNetwork ([Expr] -> Expr)
type Vector = [Expr] -> Expr
type Matrix = [Expr] -> Expr
type NodeArray = [Expr] -> Expr
tcase :: Term a -> [Term a] -> Term a
(//) :: BayesianNetwork Expr -> String -> BayesianNetwork Expr
buildBayesianGraph :: BayesianNetwork a -> (a, BayesianGraph)

-- | Pretty printing
class Show t => PP t
ppPrec :: PP t => Rational -> t -> Doc
pp :: PP t => t -> Doc
class LaTeX t
latex :: LaTeX t => t -> Doc
simulate :: FilePath -> BayesianSimulator () -> IO ()
genSimulator :: FilePath -> BayesianSimulator () -> IO ()
setSampleCount :: Int -> BayesianSimulator ()
setIterationsPerSample :: Int -> BayesianSimulator ()
setWarmupCount :: Int -> BayesianSimulator ()
setThreadNum :: Int -> BayesianSimulator ()
useMersenneTwister :: Bool -> BayesianSimulator ()
enableProfiling :: Bool -> BayesianSimulator ()

-- | Set the random seed for a thread. This function may be calledd
--   multiple times to set the seeds for multiple threads. The seeds are
--   used in order: first call is for thread 0, next for thread 1, etc.
setRandomSeed :: Int -> BayesianSimulator ()

-- | When using a specialized slizer, we generate a custom slicer for each
--   stochastic variable. The benefit of this is that, in principle, this
--   may result in more efficient code, at the cost of longer compilation
--   time, and larger binary. The alternative is to use a generic slicing
--   function which is parameterized by the log-likelihood function for a
--   variable.
useSpecialSlicers :: Bool -> BayesianSimulator ()

-- | Generate a separate file for each stochastic variable. The benefit of
--   this flag is that it makes it possible to compile multiple files in
--   parallel. The drawback is that some optimizations may be lost because
--   the files are compiled separately. Also, there is some overhead for
--   processing multiple files.
splitFiles :: Bool -> BayesianSimulator ()
model :: BayesianNetwork a -> BayesianSimulator a
observe :: Term NodeIdx -> Double -> BayesianSimulator ()
monitor :: String -> Expr -> BayesianSimulator ()

-- | Like monitor, but adds the indexes in the label of the variable.
monitorVec :: String -> Matrix -> [Int] -> BayesianSimulator ()
monitorVecs :: String -> NodeArray -> [[Int]] -> BayesianSimulator ()
runLatex :: BayesianNetwork a -> IO ()