Monadic wrapper for
- data CondensedTable v a
- type CondensedTableV = CondensedTable Vector
- type CondensedTableU = CondensedTable Vector
- genFromTable :: (PrimMonad m, Vector v a) => CondensedTable v a -> Rand m a
- tableFromProbabilities :: (Vector v (a, Word32), Vector v (a, Double), Vector v a, Vector v Word32, Show a) => v (a, Double) -> CondensedTable v a
- tableFromWeights :: (Vector v (a, Word32), Vector v (a, Double), Vector v a, Vector v Word32, Show a) => v (a, Double) -> CondensedTable v a
- tableFromIntWeights :: (Vector v (a, Word32), Vector v a, Vector v Word32) => v (a, Word32) -> CondensedTable v a
- tablePoisson :: Double -> CondensedTableU Int
- tableBinomial :: Int -> Double -> CondensedTableU Int
data CondensedTable v a
A lookup table for arbitrary discrete distributions. It allows
the generation of random variates in O(1). Note that probability
is quantized in units of
1/2^32, and all distributions with
infinite support (e.g. Poisson) should be truncated.
CondensedTable that uses boxed vectors, and is able to hold
any type of element.
Constructors for tables
Generate a condensed lookup table from a list of outcomes with given probabilities. The vector should be non-empty and the probabilites should be non-negative and sum to 1. If this is not the case, this algorithm will construct a table for some distribution that may bear no resemblance to what you intended.
tableFromProbabilities but treats number as weights not
probilities. Non-positive weights are discarded, and those
remaining are normalized to 1.
Generate a condensed lookup table from integer weights. Weights
should sum to
2^32. If they don't, the algorithm will alter the
weights so that they do. This approach should work reasonably well
for rounding errors.
Create a lookup table for the Poisson distibution. Note that table construction may have significant cost. For λ < 100 it takes as much time to build table as generation of 1000-30000 variates.