package prbnmcn-stats

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prbnmcn-stats
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    Stateful_PRNG

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Generative distributions.

module Make
  (RNG : Stats_intf.Stateful_PRNG) : 
  Stats_intf.Gen with type state = RNG.t

Implementation of generative distributions parameterized by stateful RNG implementation.

Instantiation of the Make functor with Random.State.

include Stats_intf.Gen with type state = Random.State.t

type state = Random.State.t

type parameters = {

nsamples : int;
state : state;

}

Follows the module type of a sampling-based monad

include Basic_structures.Basic_intf.Monad
  with type 'a t = (state, 'a) Stats_intf.gen
   and type 'a res =
         parameters ->
         (module Basic_structures.Basic_intf.Std with type t = 'a) ->
         ('a, float) Stats_intf.fin_mes

type 'a t = (state, 'a) Stats_intf.gen

'a t is the type of computations of type 'a

type 'a res =
  parameters ->
  (module Basic_structures.Basic_intf.Std with type t = 'a) ->
  ('a, float) Stats_intf.fin_mes

'a res is the outcome of running a computation of type 'a

val return : 'a -> 'a t

return x injects a value x as a computation

val bind : 'a t -> ('a -> 'b t) -> 'b t

Monadic bind

val map : 'a t -> ('a -> 'b) -> 'b t

Functorial map

val run : 'a t -> 'a res

Running a monadic computation

module Infix : sig ... end

val float : float -> float t

float bound samples uniformly in 0; bound

val int : int -> int t

int bound samples uniformly in 0; bound-1

val bool : bool t

bool samples a boolean uniformly

val uniform : 'a array -> 'a t

uniform elts samples an element of the elts array by sampling an index uniformly.

raises Invalid_argument
if elts has length equal to 0

val bernouilli : float -> bool t

bernouilli alpha samples true with probability alpha.

raises Invalid_argument
if alpha is not in the 0;1 interval.

val geometric : float -> int t

geometric p samples a nonnegative integer according to the geometric law of parameter p.

raises Invalid_argument
if p <= 0 || p > 1

val subsample : n:int -> 'a t -> 'a t

subsample ~n gen samples one out of n samples from gen.

val of_empirical : 'a Stats_intf.emp -> 'a t

of_empirical emp samples from emp matching exactly the empirical frequencies.

val exponential : rate:float -> float t

Exponential distribution via inverse CDF.

raises Invalid_argument
if rate <=. 0.0.

val box_muller : mean:float -> std:float -> (float * float) t

Gaussian distribution via Box-Muller transform. Returns a pair of independent gaussian variates with prescribed mean and standard deviation.

raises Invalid_argument
if std <= 0.0

val gaussian : mean:float -> std:float -> float t

Gaussian distribution (wrapper over box-muller transform).

raises Invalid_argument
if std <= 0.0

val poisson : lambda:float -> int t

Poisson distribution via inverse transform. Consider using other methods for large lambda.

raises Invalid_argument
if lambda <= 0.0

val range : Stats_intf.range -> float t

Samples uniformly in the given range.

raises Invalid_argument
if range is empty.

val gamma : shape:float -> scale:float -> float t

Gamma distribution.

val categorical : ('a * float) list -> 'a t

Categorical distribution. Total mass need not be one. Does not aggregate mass of equal elements.

raises Invalid_argument
if some weights are negative or if the total mass is zero.

val without_replacement : int -> 'a list -> ('a list * 'a list) t

sample_without_replacement n l samples a subset of size n from l without replacement.

raises Invalid_argument
if n > List.length l.

val of_fin_mes : ('a, float) Stats_intf.fin_mes -> 'a t

Constructs a sampler from a finite measure through categorical. NB: this is mostly useful for sampling repeatedly from the distribution.

module Rational : sig ... end