Safe Haskell	Safe-Inferred
Language	Haskell2010

Data.Random.Distribution

Synopsis

class Distribution d t where
- rvar :: d t -> RVar t
- rvarT :: d t -> RVarT n t
class Distribution d t => PDF d t where
- pdf :: d t -> t -> Double
- logPdf :: d t -> t -> Double
class Distribution d t => CDF d t where
- cdf :: d t -> t -> Double

Documentation

class Distribution d t where Source #

A Distribution is a data representation of a random variable's probability structure. For example, in Data.Random.Distribution.Normal, the Normal distribution is defined as:

data Normal a
    = StdNormal
    | Normal a a

Where the two parameters of the Normal data constructor are the mean and standard deviation of the random variable, respectively. To make use of the Normal type, one can convert it to an rvar and manipulate it or sample it directly:

x <- sample (rvar (Normal 10 2))
x <- sample (Normal 10 2)

A Distribution is typically more transparent than an RVar but less composable (precisely because of that transparency). There are several practical uses for types implementing Distribution:

Typically, a Distribution will expose several parameters of a standard mathematical model of a probability distribution, such as mean and std deviation for the normal distribution. Thus, they can be manipulated analytically using mathematical insights about the distributions they represent. For example, a collection of bernoulli variables could be simplified into a (hopefully) smaller collection of binomial variables.
Because they are generally just containers for parameters, they can be easily serialized to persistent storage or read from user-supplied configurations (eg, initialization data for a simulation).
If a type additionally implements the CDF subclass, which extends Distribution with a cumulative density function, an arbitrary random variable x can be tested against the distribution by testing fmap (cdf dist) x for uniformity.

On the other hand, most Distributions will not be closed under all the same operations as RVar (which, being a monad, has a fully turing-complete internal computational model). The sum of two uniformly-distributed variables, for example, is not uniformly distributed. To support general composition, the Distribution class defines a function rvar to construct the more-abstract and more-composable RVar representation of a random variable.

Minimal complete definition

Nothing

Methods

rvar :: d t -> RVar t Source #

Return a random variable with this distribution.

rvarT :: d t -> RVarT n t Source #

Return a random variable with the given distribution, pre-lifted to an arbitrary RVarT. Any arbitrary RVar can also be converted to an 'RVarT m' for an arbitrary m, using either lift or sample.

Instances

Instances details

Distribution StdUniform Bool Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Bool -> RVar Bool Source # rvarT :: forall (n :: Type -> Type). StdUniform Bool -> RVarT n Bool Source #
Distribution StdUniform Char Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Char -> RVar Char Source # rvarT :: forall (n :: Type -> Type). StdUniform Char -> RVarT n Char Source #
Distribution StdUniform Double Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Double -> RVar Double Source # rvarT :: forall (n :: Type -> Type). StdUniform Double -> RVarT n Double Source #
Distribution StdUniform Float Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Float -> RVar Float Source # rvarT :: forall (n :: Type -> Type). StdUniform Float -> RVarT n Float Source #
Distribution StdUniform Int Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Int -> RVar Int Source # rvarT :: forall (n :: Type -> Type). StdUniform Int -> RVarT n Int Source #
Distribution StdUniform Int8 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Int8 -> RVar Int8 Source # rvarT :: forall (n :: Type -> Type). StdUniform Int8 -> RVarT n Int8 Source #
Distribution StdUniform Int16 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Int16 -> RVar Int16 Source # rvarT :: forall (n :: Type -> Type). StdUniform Int16 -> RVarT n Int16 Source #
Distribution StdUniform Int32 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Int32 -> RVar Int32 Source # rvarT :: forall (n :: Type -> Type). StdUniform Int32 -> RVarT n Int32 Source #
Distribution StdUniform Int64 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Int64 -> RVar Int64 Source # rvarT :: forall (n :: Type -> Type). StdUniform Int64 -> RVarT n Int64 Source #
Distribution StdUniform Ordering Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Ordering -> RVar Ordering Source # rvarT :: forall (n :: Type -> Type). StdUniform Ordering -> RVarT n Ordering Source #
Distribution StdUniform Word Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Word -> RVar Word Source # rvarT :: forall (n :: Type -> Type). StdUniform Word -> RVarT n Word Source #
Distribution StdUniform Word8 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Word8 -> RVar Word8 Source # rvarT :: forall (n :: Type -> Type). StdUniform Word8 -> RVarT n Word8 Source #
Distribution StdUniform Word16 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Word16 -> RVar Word16 Source # rvarT :: forall (n :: Type -> Type). StdUniform Word16 -> RVarT n Word16 Source #
Distribution StdUniform Word32 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Word32 -> RVar Word32 Source # rvarT :: forall (n :: Type -> Type). StdUniform Word32 -> RVarT n Word32 Source #
Distribution StdUniform Word64 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform Word64 -> RVar Word64 Source # rvarT :: forall (n :: Type -> Type). StdUniform Word64 -> RVarT n Word64 Source #
Distribution StdUniform () Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform () -> RVar () Source # rvarT :: forall (n :: Type -> Type). StdUniform () -> RVarT n () Source #
Distribution Uniform Bool Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Bool -> RVar Bool Source # rvarT :: forall (n :: Type -> Type). Uniform Bool -> RVarT n Bool Source #
Distribution Uniform Char Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Char -> RVar Char Source # rvarT :: forall (n :: Type -> Type). Uniform Char -> RVarT n Char Source #
Distribution Uniform Double Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Double -> RVar Double Source # rvarT :: forall (n :: Type -> Type). Uniform Double -> RVarT n Double Source #
Distribution Uniform Float Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Float -> RVar Float Source # rvarT :: forall (n :: Type -> Type). Uniform Float -> RVarT n Float Source #
Distribution Uniform Int Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Int -> RVar Int Source # rvarT :: forall (n :: Type -> Type). Uniform Int -> RVarT n Int Source #
Distribution Uniform Int8 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Int8 -> RVar Int8 Source # rvarT :: forall (n :: Type -> Type). Uniform Int8 -> RVarT n Int8 Source #
Distribution Uniform Int16 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Int16 -> RVar Int16 Source # rvarT :: forall (n :: Type -> Type). Uniform Int16 -> RVarT n Int16 Source #
Distribution Uniform Int32 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Int32 -> RVar Int32 Source # rvarT :: forall (n :: Type -> Type). Uniform Int32 -> RVarT n Int32 Source #
Distribution Uniform Int64 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Int64 -> RVar Int64 Source # rvarT :: forall (n :: Type -> Type). Uniform Int64 -> RVarT n Int64 Source #
Distribution Uniform Integer Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Integer -> RVar Integer Source # rvarT :: forall (n :: Type -> Type). Uniform Integer -> RVarT n Integer Source #
Distribution Uniform Ordering Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Ordering -> RVar Ordering Source # rvarT :: forall (n :: Type -> Type). Uniform Ordering -> RVarT n Ordering Source #
Distribution Uniform Word Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Word -> RVar Word Source # rvarT :: forall (n :: Type -> Type). Uniform Word -> RVarT n Word Source #
Distribution Uniform Word8 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Word8 -> RVar Word8 Source # rvarT :: forall (n :: Type -> Type). Uniform Word8 -> RVarT n Word8 Source #
Distribution Uniform Word16 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Word16 -> RVar Word16 Source # rvarT :: forall (n :: Type -> Type). Uniform Word16 -> RVarT n Word16 Source #
Distribution Uniform Word32 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Word32 -> RVar Word32 Source # rvarT :: forall (n :: Type -> Type). Uniform Word32 -> RVarT n Word32 Source #
Distribution Uniform Word64 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform Word64 -> RVar Word64 Source # rvarT :: forall (n :: Type -> Type). Uniform Word64 -> RVarT n Word64 Source #
Distribution Uniform () Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform () -> RVar () Source # rvarT :: forall (n :: Type -> Type). Uniform () -> RVarT n () Source #
(Floating a, Distribution StdUniform a) => Distribution Weibull a Source #
Instance details Defined in Data.Random.Distribution.Weibull Methods rvar :: Weibull a -> RVar a Source # rvarT :: forall (n :: Type -> Type). Weibull a -> RVarT n a Source #
(RealFloat a, Ord a, Distribution StdUniform a) => Distribution Triangular a Source #
Instance details Defined in Data.Random.Distribution.Triangular Methods rvar :: Triangular a -> RVar a Source # rvarT :: forall (n :: Type -> Type). Triangular a -> RVarT n a Source #
(Floating a, Distribution StdUniform a) => Distribution StretchedExponential a Source #
Instance details Defined in Data.Random.Distribution.StretchedExponential Methods rvar :: StretchedExponential a -> RVar a Source # rvarT :: forall (n :: Type -> Type). StretchedExponential a -> RVarT n a Source #
(RealFloat a, Distribution StdUniform a) => Distribution Rayleigh a Source #
Instance details Defined in Data.Random.Distribution.Rayleigh Methods rvar :: Rayleigh a -> RVar a Source # rvarT :: forall (n :: Type -> Type). Rayleigh a -> RVarT n a Source #
Distribution Normal Double Source #
Instance details Defined in Data.Random.Distribution.Normal Methods rvar :: Normal Double -> RVar Double Source # rvarT :: forall (n :: Type -> Type). Normal Double -> RVarT n Double Source #
Distribution Normal Float Source #
Instance details Defined in Data.Random.Distribution.Normal Methods rvar :: Normal Float -> RVar Float Source # rvarT :: forall (n :: Type -> Type). Normal Float -> RVarT n Float Source #
(Floating a, Ord a, Distribution Normal a, Distribution StdUniform a) => Distribution Gamma a Source #
Instance details Defined in Data.Random.Distribution.Gamma Methods rvar :: Gamma a -> RVar a Source # rvarT :: forall (n :: Type -> Type). Gamma a -> RVarT n a Source #
(Floating a, Distribution StdUniform a) => Distribution Exponential a Source #
Instance details Defined in Data.Random.Distribution.Exponential Methods rvar :: Exponential a -> RVar a Source # rvarT :: forall (n :: Type -> Type). Exponential a -> RVarT n a Source #
(Fractional t, Distribution Gamma t) => Distribution ChiSquare t Source #
Instance details Defined in Data.Random.Distribution.ChiSquare Methods rvar :: ChiSquare t -> RVar t Source # rvarT :: forall (n :: Type -> Type). ChiSquare t -> RVarT n t Source #
(Floating a, Distribution Normal a, Distribution ChiSquare a) => Distribution T a Source #
Instance details Defined in Data.Random.Distribution.T Methods rvar :: T a -> RVar a Source # rvarT :: forall (n :: Type -> Type). T a -> RVarT n a Source #
Distribution Beta Double Source #
Instance details Defined in Data.Random.Distribution.Beta Methods rvar :: Beta Double -> RVar Double Source # rvarT :: forall (n :: Type -> Type). Beta Double -> RVarT n Double Source #
Distribution Beta Float Source #
Instance details Defined in Data.Random.Distribution.Beta Methods rvar :: Beta Float -> RVar Float Source # rvarT :: forall (n :: Type -> Type). Beta Float -> RVarT n Float Source #
(Floating a, Distribution StdUniform a) => Distribution Pareto a Source #
Instance details Defined in Data.Random.Distribution.Pareto Methods rvar :: Pareto a -> RVar a Source # rvarT :: forall (n :: Type -> Type). Pareto a -> RVarT n a Source #
(Ord a, Fractional a, Distribution StdUniform a) => Distribution StdSimplex [a] Source #
Instance details Defined in Data.Random.Distribution.Simplex Methods rvar :: StdSimplex [a] -> RVar [a] Source # rvarT :: forall (n :: Type -> Type). StdSimplex [a] -> RVarT n [a] Source #
(Fractional a, Distribution Gamma a) => Distribution Dirichlet [a] Source #
Instance details Defined in Data.Random.Distribution.Dirichlet Methods rvar :: Dirichlet [a] -> RVar [a] Source # rvarT :: forall (n :: Type -> Type). Dirichlet [a] -> RVarT n [a] Source #
HasResolution r => Distribution StdUniform (Fixed r) Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: StdUniform (Fixed r) -> RVar (Fixed r) Source # rvarT :: forall (n :: Type -> Type). StdUniform (Fixed r) -> RVarT n (Fixed r) Source #
HasResolution r => Distribution Uniform (Fixed r) Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods rvar :: Uniform (Fixed r) -> RVar (Fixed r) Source # rvarT :: forall (n :: Type -> Type). Uniform (Fixed r) -> RVarT n (Fixed r) Source #
(Num t, Ord t, Vector v t) => Distribution (Ziggurat v) t Source #
Instance details Defined in Data.Random.Distribution.Ziggurat Methods rvar :: Ziggurat v t -> RVar t Source # rvarT :: forall (n :: Type -> Type). Ziggurat v t -> RVarT n t Source #
(Integral a, Floating b, Ord b, Distribution Normal b, Distribution StdUniform b) => Distribution (Erlang a) b Source #
Instance details Defined in Data.Random.Distribution.Gamma Methods rvar :: Erlang a b -> RVar b Source # rvarT :: forall (n :: Type -> Type). Erlang a b -> RVarT n b Source #
(Fractional p, Ord p, Distribution Uniform p) => Distribution (Categorical p) a Source #
Instance details Defined in Data.Random.Distribution.Categorical Methods rvar :: Categorical p a -> RVar a Source # rvarT :: forall (n :: Type -> Type). Categorical p a -> RVarT n a Source #
Distribution (Binomial b) Integer => Distribution (Binomial b) Double Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Double -> RVar Double Source # rvarT :: forall (n :: Type -> Type). Binomial b Double -> RVarT n Double Source #
Distribution (Binomial b) Integer => Distribution (Binomial b) Float Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Float -> RVar Float Source # rvarT :: forall (n :: Type -> Type). Binomial b Float -> RVarT n Float Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Word64 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Word64 -> RVar Word64 Source # rvarT :: forall (n :: Type -> Type). Binomial b Word64 -> RVarT n Word64 Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Word32 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Word32 -> RVar Word32 Source # rvarT :: forall (n :: Type -> Type). Binomial b Word32 -> RVarT n Word32 Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Word16 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Word16 -> RVar Word16 Source # rvarT :: forall (n :: Type -> Type). Binomial b Word16 -> RVarT n Word16 Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Word8 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Word8 -> RVar Word8 Source # rvarT :: forall (n :: Type -> Type). Binomial b Word8 -> RVarT n Word8 Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Word Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Word -> RVar Word Source # rvarT :: forall (n :: Type -> Type). Binomial b Word -> RVarT n Word Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Int64 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Int64 -> RVar Int64 Source # rvarT :: forall (n :: Type -> Type). Binomial b Int64 -> RVarT n Int64 Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Int32 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Int32 -> RVar Int32 Source # rvarT :: forall (n :: Type -> Type). Binomial b Int32 -> RVarT n Int32 Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Int16 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Int16 -> RVar Int16 Source # rvarT :: forall (n :: Type -> Type). Binomial b Int16 -> RVarT n Int16 Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Int8 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Int8 -> RVar Int8 Source # rvarT :: forall (n :: Type -> Type). Binomial b Int8 -> RVarT n Int8 Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Int Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Int -> RVar Int Source # rvarT :: forall (n :: Type -> Type). Binomial b Int -> RVarT n Int Source #
(Floating b, Ord b, Distribution Beta b, Distribution StdUniform b) => Distribution (Binomial b) Integer Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods rvar :: Binomial b Integer -> RVar Integer Source # rvarT :: forall (n :: Type -> Type). Binomial b Integer -> RVarT n Integer Source #
Distribution (Poisson b) Integer => Distribution (Poisson b) Double Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Double -> RVar Double Source # rvarT :: forall (n :: Type -> Type). Poisson b Double -> RVarT n Double Source #
Distribution (Poisson b) Integer => Distribution (Poisson b) Float Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Float -> RVar Float Source # rvarT :: forall (n :: Type -> Type). Poisson b Float -> RVarT n Float Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Word64) b, Distribution (Binomial b) Word64) => Distribution (Poisson b) Word64 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Word64 -> RVar Word64 Source # rvarT :: forall (n :: Type -> Type). Poisson b Word64 -> RVarT n Word64 Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Word32) b, Distribution (Binomial b) Word32) => Distribution (Poisson b) Word32 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Word32 -> RVar Word32 Source # rvarT :: forall (n :: Type -> Type). Poisson b Word32 -> RVarT n Word32 Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Word16) b, Distribution (Binomial b) Word16) => Distribution (Poisson b) Word16 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Word16 -> RVar Word16 Source # rvarT :: forall (n :: Type -> Type). Poisson b Word16 -> RVarT n Word16 Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Word8) b, Distribution (Binomial b) Word8) => Distribution (Poisson b) Word8 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Word8 -> RVar Word8 Source # rvarT :: forall (n :: Type -> Type). Poisson b Word8 -> RVarT n Word8 Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Word) b, Distribution (Binomial b) Word) => Distribution (Poisson b) Word Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Word -> RVar Word Source # rvarT :: forall (n :: Type -> Type). Poisson b Word -> RVarT n Word Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Int64) b, Distribution (Binomial b) Int64) => Distribution (Poisson b) Int64 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Int64 -> RVar Int64 Source # rvarT :: forall (n :: Type -> Type). Poisson b Int64 -> RVarT n Int64 Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Int32) b, Distribution (Binomial b) Int32) => Distribution (Poisson b) Int32 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Int32 -> RVar Int32 Source # rvarT :: forall (n :: Type -> Type). Poisson b Int32 -> RVarT n Int32 Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Int16) b, Distribution (Binomial b) Int16) => Distribution (Poisson b) Int16 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Int16 -> RVar Int16 Source # rvarT :: forall (n :: Type -> Type). Poisson b Int16 -> RVarT n Int16 Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Int8) b, Distribution (Binomial b) Int8) => Distribution (Poisson b) Int8 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Int8 -> RVar Int8 Source # rvarT :: forall (n :: Type -> Type). Poisson b Int8 -> RVarT n Int8 Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Int) b, Distribution (Binomial b) Int) => Distribution (Poisson b) Int Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Int -> RVar Int Source # rvarT :: forall (n :: Type -> Type). Poisson b Int -> RVarT n Int Source #
(RealFloat b, Distribution StdUniform b, Distribution (Erlang Integer) b, Distribution (Binomial b) Integer) => Distribution (Poisson b) Integer Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods rvar :: Poisson b Integer -> RVar Integer Source # rvarT :: forall (n :: Type -> Type). Poisson b Integer -> RVarT n Integer Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Double Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Double -> RVar Double Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Double -> RVarT n Double Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Float Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Float -> RVar Float Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Float -> RVarT n Float Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Word64 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Word64 -> RVar Word64 Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Word64 -> RVarT n Word64 Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Word32 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Word32 -> RVar Word32 Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Word32 -> RVarT n Word32 Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Word16 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Word16 -> RVar Word16 Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Word16 -> RVarT n Word16 Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Word8 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Word8 -> RVar Word8 Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Word8 -> RVarT n Word8 Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Word Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Word -> RVar Word Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Word -> RVarT n Word Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Int64 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Int64 -> RVar Int64 Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Int64 -> RVarT n Int64 Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Int32 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Int32 -> RVar Int32 Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Int32 -> RVarT n Int32 Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Int16 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Int16 -> RVar Int16 Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Int16 -> RVarT n Int16 Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Int8 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Int8 -> RVar Int8 Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Int8 -> RVarT n Int8 Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Int Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Int -> RVar Int Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Int -> RVarT n Int Source #
Distribution (Bernoulli b) Bool => Distribution (Bernoulli b) Integer Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Integer -> RVar Integer Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Integer -> RVarT n Integer Source #
(Fractional b, Ord b, Distribution StdUniform b) => Distribution (Bernoulli b) Bool Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b Bool -> RVar Bool Source # rvarT :: forall (n :: Type -> Type). Bernoulli b Bool -> RVarT n Bool Source #
(Num a, Eq a, Fractional p, Distribution (Binomial p) a) => Distribution (Multinomial p) [a] Source #
Instance details Defined in Data.Random.Distribution.Multinomial Methods rvar :: Multinomial p [a] -> RVar [a] Source # rvarT :: forall (n :: Type -> Type). Multinomial p [a] -> RVarT n [a] Source #
(Distribution (Bernoulli b) Bool, RealFloat a) => Distribution (Bernoulli b) (Complex a) Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b (Complex a) -> RVar (Complex a) Source # rvarT :: forall (n :: Type -> Type). Bernoulli b (Complex a) -> RVarT n (Complex a) Source #
(Distribution (Bernoulli b) Bool, Integral a) => Distribution (Bernoulli b) (Ratio a) Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods rvar :: Bernoulli b (Ratio a) -> RVar (Ratio a) Source # rvarT :: forall (n :: Type -> Type). Bernoulli b (Ratio a) -> RVarT n (Ratio a) Source #

class Distribution d t => PDF d t where Source #

Minimal complete definition

Nothing

Methods

pdf :: d t -> t -> Double Source #

logPdf :: d t -> t -> Double Source #

Instances

Instances details

PDF StdUniform Double Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods pdf :: StdUniform Double -> Double -> Double Source # logPdf :: StdUniform Double -> Double -> Double Source #
PDF StdUniform Float Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods pdf :: StdUniform Float -> Float -> Double Source # logPdf :: StdUniform Float -> Float -> Double Source #
(Real a, Floating a, Distribution Normal a) => PDF Normal a Source #
Instance details Defined in Data.Random.Distribution.Normal Methods pdf :: Normal a -> a -> Double Source # logPdf :: Normal a -> a -> Double Source #
PDF Beta Double Source #
Instance details Defined in Data.Random.Distribution.Beta Methods pdf :: Beta Double -> Double -> Double Source # logPdf :: Beta Double -> Double -> Double Source #
PDF Beta Float Source #
Instance details Defined in Data.Random.Distribution.Beta Methods pdf :: Beta Float -> Float -> Double Source # logPdf :: Beta Float -> Float -> Double Source #
PDF (Binomial b) Integer => PDF (Binomial b) Double Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Double -> Double -> Double Source # logPdf :: Binomial b Double -> Double -> Double Source #
PDF (Binomial b) Integer => PDF (Binomial b) Float Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Float -> Float -> Double Source # logPdf :: Binomial b Float -> Float -> Double Source #
(Real b, Distribution (Binomial b) Word64) => PDF (Binomial b) Word64 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Word64 -> Word64 -> Double Source # logPdf :: Binomial b Word64 -> Word64 -> Double Source #
(Real b, Distribution (Binomial b) Word32) => PDF (Binomial b) Word32 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Word32 -> Word32 -> Double Source # logPdf :: Binomial b Word32 -> Word32 -> Double Source #
(Real b, Distribution (Binomial b) Word16) => PDF (Binomial b) Word16 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Word16 -> Word16 -> Double Source # logPdf :: Binomial b Word16 -> Word16 -> Double Source #
(Real b, Distribution (Binomial b) Word8) => PDF (Binomial b) Word8 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Word8 -> Word8 -> Double Source # logPdf :: Binomial b Word8 -> Word8 -> Double Source #
(Real b, Distribution (Binomial b) Word) => PDF (Binomial b) Word Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Word -> Word -> Double Source # logPdf :: Binomial b Word -> Word -> Double Source #
(Real b, Distribution (Binomial b) Int64) => PDF (Binomial b) Int64 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Int64 -> Int64 -> Double Source # logPdf :: Binomial b Int64 -> Int64 -> Double Source #
(Real b, Distribution (Binomial b) Int32) => PDF (Binomial b) Int32 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Int32 -> Int32 -> Double Source # logPdf :: Binomial b Int32 -> Int32 -> Double Source #
(Real b, Distribution (Binomial b) Int16) => PDF (Binomial b) Int16 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Int16 -> Int16 -> Double Source # logPdf :: Binomial b Int16 -> Int16 -> Double Source #
(Real b, Distribution (Binomial b) Int8) => PDF (Binomial b) Int8 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Int8 -> Int8 -> Double Source # logPdf :: Binomial b Int8 -> Int8 -> Double Source #
(Real b, Distribution (Binomial b) Int) => PDF (Binomial b) Int Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Int -> Int -> Double Source # logPdf :: Binomial b Int -> Int -> Double Source #
(Real b, Distribution (Binomial b) Integer) => PDF (Binomial b) Integer Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods pdf :: Binomial b Integer -> Integer -> Double Source # logPdf :: Binomial b Integer -> Integer -> Double Source #
PDF (Poisson b) Integer => PDF (Poisson b) Double Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Double -> Double -> Double Source # logPdf :: Poisson b Double -> Double -> Double Source #
PDF (Poisson b) Integer => PDF (Poisson b) Float Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Float -> Float -> Double Source # logPdf :: Poisson b Float -> Float -> Double Source #
(Real b, Distribution (Poisson b) Word64) => PDF (Poisson b) Word64 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Word64 -> Word64 -> Double Source # logPdf :: Poisson b Word64 -> Word64 -> Double Source #
(Real b, Distribution (Poisson b) Word32) => PDF (Poisson b) Word32 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Word32 -> Word32 -> Double Source # logPdf :: Poisson b Word32 -> Word32 -> Double Source #
(Real b, Distribution (Poisson b) Word16) => PDF (Poisson b) Word16 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Word16 -> Word16 -> Double Source # logPdf :: Poisson b Word16 -> Word16 -> Double Source #
(Real b, Distribution (Poisson b) Word8) => PDF (Poisson b) Word8 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Word8 -> Word8 -> Double Source # logPdf :: Poisson b Word8 -> Word8 -> Double Source #
(Real b, Distribution (Poisson b) Word) => PDF (Poisson b) Word Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Word -> Word -> Double Source # logPdf :: Poisson b Word -> Word -> Double Source #
(Real b, Distribution (Poisson b) Int64) => PDF (Poisson b) Int64 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Int64 -> Int64 -> Double Source # logPdf :: Poisson b Int64 -> Int64 -> Double Source #
(Real b, Distribution (Poisson b) Int32) => PDF (Poisson b) Int32 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Int32 -> Int32 -> Double Source # logPdf :: Poisson b Int32 -> Int32 -> Double Source #
(Real b, Distribution (Poisson b) Int16) => PDF (Poisson b) Int16 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Int16 -> Int16 -> Double Source # logPdf :: Poisson b Int16 -> Int16 -> Double Source #
(Real b, Distribution (Poisson b) Int8) => PDF (Poisson b) Int8 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Int8 -> Int8 -> Double Source # logPdf :: Poisson b Int8 -> Int8 -> Double Source #
(Real b, Distribution (Poisson b) Int) => PDF (Poisson b) Int Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Int -> Int -> Double Source # logPdf :: Poisson b Int -> Int -> Double Source #
(Real b, Distribution (Poisson b) Integer) => PDF (Poisson b) Integer Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods pdf :: Poisson b Integer -> Integer -> Double Source # logPdf :: Poisson b Integer -> Integer -> Double Source #

class Distribution d t => CDF d t where Source #

Methods

cdf :: d t -> t -> Double Source #

Return the cumulative distribution function of this distribution. That is, a function taking x :: t to the probability that the next sample will return a value less than or equal to x, according to some order or partial order (not necessarily an obvious one).

In the case where t is an instance of Ord, cdf should correspond to the CDF with respect to that order.

In other cases, cdf is only required to satisfy the following law: fmap (cdf d) (rvar d) must be uniformly distributed over (0,1). Inclusion of either endpoint is optional, though the preferred range is (0,1].

Note that this definition requires that cdf for a product type should _not_ be a joint CDF as commonly defined, as that definition violates both conditions. Instead, it should be a univariate CDF over the product type. That is, it should represent the CDF with respect to the lexicographic order of the product.

The present specification is probably only really useful for testing conformance of a variable to its target distribution, and I am open to suggestions for more-useful specifications (especially with regard to the interaction with product types).

Instances

Instances details

CDF StdUniform Bool Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Bool -> Bool -> Double Source #
CDF StdUniform Char Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Char -> Char -> Double Source #
CDF StdUniform Double Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Double -> Double -> Double Source #
CDF StdUniform Float Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Float -> Float -> Double Source #
CDF StdUniform Int Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Int -> Int -> Double Source #
CDF StdUniform Int8 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Int8 -> Int8 -> Double Source #
CDF StdUniform Int16 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Int16 -> Int16 -> Double Source #
CDF StdUniform Int32 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Int32 -> Int32 -> Double Source #
CDF StdUniform Int64 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Int64 -> Int64 -> Double Source #
CDF StdUniform Ordering Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Ordering -> Ordering -> Double Source #
CDF StdUniform Word Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Word -> Word -> Double Source #
CDF StdUniform Word8 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Word8 -> Word8 -> Double Source #
CDF StdUniform Word16 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Word16 -> Word16 -> Double Source #
CDF StdUniform Word32 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Word32 -> Word32 -> Double Source #
CDF StdUniform Word64 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform Word64 -> Word64 -> Double Source #
CDF StdUniform () Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform () -> () -> Double Source #
CDF Uniform Bool Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Bool -> Bool -> Double Source #
CDF Uniform Char Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Char -> Char -> Double Source #
CDF Uniform Double Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Double -> Double -> Double Source #
CDF Uniform Float Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Float -> Float -> Double Source #
CDF Uniform Int Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Int -> Int -> Double Source #
CDF Uniform Int8 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Int8 -> Int8 -> Double Source #
CDF Uniform Int16 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Int16 -> Int16 -> Double Source #
CDF Uniform Int32 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Int32 -> Int32 -> Double Source #
CDF Uniform Int64 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Int64 -> Int64 -> Double Source #
CDF Uniform Integer Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Integer -> Integer -> Double Source #
CDF Uniform Ordering Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Ordering -> Ordering -> Double Source #
CDF Uniform Word Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Word -> Word -> Double Source #
CDF Uniform Word8 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Word8 -> Word8 -> Double Source #
CDF Uniform Word16 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Word16 -> Word16 -> Double Source #
CDF Uniform Word32 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Word32 -> Word32 -> Double Source #
CDF Uniform Word64 Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform Word64 -> Word64 -> Double Source #
CDF Uniform () Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform () -> () -> Double Source #
(Real a, Distribution Weibull a) => CDF Weibull a Source #
Instance details Defined in Data.Random.Distribution.Weibull Methods cdf :: Weibull a -> a -> Double Source #
(RealFrac a, Distribution Triangular a) => CDF Triangular a Source #
Instance details Defined in Data.Random.Distribution.Triangular Methods cdf :: Triangular a -> a -> Double Source #
(Real a, Distribution StretchedExponential a) => CDF StretchedExponential a Source #
Instance details Defined in Data.Random.Distribution.StretchedExponential Methods cdf :: StretchedExponential a -> a -> Double Source #
(Real a, Distribution Rayleigh a) => CDF Rayleigh a Source #
Instance details Defined in Data.Random.Distribution.Rayleigh Methods cdf :: Rayleigh a -> a -> Double Source #
(Real a, Distribution Normal a) => CDF Normal a Source #
Instance details Defined in Data.Random.Distribution.Normal Methods cdf :: Normal a -> a -> Double Source #
(Real a, Distribution Gamma a) => CDF Gamma a Source #
Instance details Defined in Data.Random.Distribution.Gamma Methods cdf :: Gamma a -> a -> Double Source #
(Real a, Distribution Exponential a) => CDF Exponential a Source #
Instance details Defined in Data.Random.Distribution.Exponential Methods cdf :: Exponential a -> a -> Double Source #
(Real t, Distribution ChiSquare t) => CDF ChiSquare t Source #
Instance details Defined in Data.Random.Distribution.ChiSquare Methods cdf :: ChiSquare t -> t -> Double Source #
(Real a, Distribution T a) => CDF T a Source #
Instance details Defined in Data.Random.Distribution.T Methods cdf :: T a -> a -> Double Source #
(Real a, Distribution Pareto a) => CDF Pareto a Source #
Instance details Defined in Data.Random.Distribution.Pareto Methods cdf :: Pareto a -> a -> Double Source #
HasResolution r => CDF StdUniform (Fixed r) Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: StdUniform (Fixed r) -> Fixed r -> Double Source #
HasResolution r => CDF Uniform (Fixed r) Source #
Instance details Defined in Data.Random.Distribution.Uniform Methods cdf :: Uniform (Fixed r) -> Fixed r -> Double Source #
(Integral a, Real b, Distribution (Erlang a) b) => CDF (Erlang a) b Source #
Instance details Defined in Data.Random.Distribution.Gamma Methods cdf :: Erlang a b -> b -> Double Source #
CDF (Binomial b) Integer => CDF (Binomial b) Double Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Double -> Double -> Double Source #
CDF (Binomial b) Integer => CDF (Binomial b) Float Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Float -> Float -> Double Source #
(Real b, Distribution (Binomial b) Word64) => CDF (Binomial b) Word64 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Word64 -> Word64 -> Double Source #
(Real b, Distribution (Binomial b) Word32) => CDF (Binomial b) Word32 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Word32 -> Word32 -> Double Source #
(Real b, Distribution (Binomial b) Word16) => CDF (Binomial b) Word16 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Word16 -> Word16 -> Double Source #
(Real b, Distribution (Binomial b) Word8) => CDF (Binomial b) Word8 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Word8 -> Word8 -> Double Source #
(Real b, Distribution (Binomial b) Word) => CDF (Binomial b) Word Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Word -> Word -> Double Source #
(Real b, Distribution (Binomial b) Int64) => CDF (Binomial b) Int64 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Int64 -> Int64 -> Double Source #
(Real b, Distribution (Binomial b) Int32) => CDF (Binomial b) Int32 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Int32 -> Int32 -> Double Source #
(Real b, Distribution (Binomial b) Int16) => CDF (Binomial b) Int16 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Int16 -> Int16 -> Double Source #
(Real b, Distribution (Binomial b) Int8) => CDF (Binomial b) Int8 Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Int8 -> Int8 -> Double Source #
(Real b, Distribution (Binomial b) Int) => CDF (Binomial b) Int Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Int -> Int -> Double Source #
(Real b, Distribution (Binomial b) Integer) => CDF (Binomial b) Integer Source #
Instance details Defined in Data.Random.Distribution.Binomial Methods cdf :: Binomial b Integer -> Integer -> Double Source #
CDF (Poisson b) Integer => CDF (Poisson b) Double Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Double -> Double -> Double Source #
CDF (Poisson b) Integer => CDF (Poisson b) Float Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Float -> Float -> Double Source #
(Real b, Distribution (Poisson b) Word64) => CDF (Poisson b) Word64 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Word64 -> Word64 -> Double Source #
(Real b, Distribution (Poisson b) Word32) => CDF (Poisson b) Word32 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Word32 -> Word32 -> Double Source #
(Real b, Distribution (Poisson b) Word16) => CDF (Poisson b) Word16 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Word16 -> Word16 -> Double Source #
(Real b, Distribution (Poisson b) Word8) => CDF (Poisson b) Word8 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Word8 -> Word8 -> Double Source #
(Real b, Distribution (Poisson b) Word) => CDF (Poisson b) Word Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Word -> Word -> Double Source #
(Real b, Distribution (Poisson b) Int64) => CDF (Poisson b) Int64 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Int64 -> Int64 -> Double Source #
(Real b, Distribution (Poisson b) Int32) => CDF (Poisson b) Int32 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Int32 -> Int32 -> Double Source #
(Real b, Distribution (Poisson b) Int16) => CDF (Poisson b) Int16 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Int16 -> Int16 -> Double Source #
(Real b, Distribution (Poisson b) Int8) => CDF (Poisson b) Int8 Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Int8 -> Int8 -> Double Source #
(Real b, Distribution (Poisson b) Int) => CDF (Poisson b) Int Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Int -> Int -> Double Source #
(Real b, Distribution (Poisson b) Integer) => CDF (Poisson b) Integer Source #
Instance details Defined in Data.Random.Distribution.Poisson Methods cdf :: Poisson b Integer -> Integer -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Double Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Double -> Double -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Float Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Float -> Float -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Word64 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Word64 -> Word64 -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Word32 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Word32 -> Word32 -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Word16 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Word16 -> Word16 -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Word8 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Word8 -> Word8 -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Word Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Word -> Word -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Int64 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Int64 -> Int64 -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Int32 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Int32 -> Int32 -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Int16 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Int16 -> Int16 -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Int8 Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Int8 -> Int8 -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Int Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Int -> Int -> Double Source #
CDF (Bernoulli b) Bool => CDF (Bernoulli b) Integer Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Integer -> Integer -> Double Source #
(Distribution (Bernoulli b) Bool, Real b) => CDF (Bernoulli b) Bool Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b Bool -> Bool -> Double Source #
(CDF (Bernoulli b) Bool, RealFloat a) => CDF (Bernoulli b) (Complex a) Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b (Complex a) -> Complex a -> Double Source #
(CDF (Bernoulli b) Bool, Integral a) => CDF (Bernoulli b) (Ratio a) Source #
Instance details Defined in Data.Random.Distribution.Bernoulli Methods cdf :: Bernoulli b (Ratio a) -> Ratio a -> Double Source #