package prbnmcn-dagger

You can search for identifiers within the package.

in-package search v0.2.0

prbnmcn-dagger
- Dagger
  - Dist
  - Internal
    
    Cps_monad
    
    Make
    
    Infix
    
    M
    
    Infix
    
    R
    
    Identity_monad
    
    M
    
    Infix
  - Intf
    
    Core
    
    Infix
    
    Field
    
    Foldable
    
    Infix
    
    S
    
    Array_ops
    
    Infix
    
    List_ops
    
    Seq_ops
  - Lmh_incremental_inference
    
    Array_ops
    
    Infix
    
    List_ops
    
    MCMC_stats
    
    Make_shared
    
    C
    
    Seq_ops
  - Lmh_inference
    
    Array_ops
    
    Infix
    
    List_ops
    
    MCMC_stats
    
    Seq_ops
  - Log_space
  - RNG
  - Resampling
    
    Make_predefined
    
    F
    
    Sampler
    
    Particles
  - Smc_inference
    
    Float_smc
    
    Array_ops
    
    Infix
    
    List_ops
    
    Seq_ops
    
    Make
    
    Array_ops
    
    Infix
    
    List_ops
    
    Resampling_types
    
    Seq_ops
    
    Resampling_types_S
    
    S
    
    Array_ops
    
    Infix
    
    List_ops
    
    Seq_ops
    
    Unit_smc
    
    Array_ops
    
    Infix
    
    List_ops
    
    Seq_ops

Legend:
Library
Module
Module type
Parameter
Class
Class type

Distributions

A distribution must be sampleable and must be associated to a density w.r.t. some base measure (which is kept implicit).

type 'a dist = private {

sample : RNG.t -> 'a;
ll : 'a -> Log_space.t;

}

'a dist is the type of distributions on the type 'a.

type 'a kernel = private {

start : 'a;
sample : 'a -> RNG.t -> 'a;
ll : 'a -> 'a -> Log_space.t;

}

'a kernel is the type of kernels (ie random walks) on the type 'a.

type 'a t = private

| Stateless of 'a dist
| Kernel of 'a kernel

A "distribution" as understood by this library is either a kernel or a plain distribution.

val stateless : (RNG.t -> 'a) -> ('a -> Log_space.t) -> 'a t

Create a distribution from a sampler and a log-density.

val kernel : 'a -> ('a -> RNG.t -> 'a) -> ('a -> 'a -> Log_space.t) -> 'a t

Create a kernel from an initial point, a kernel and its associated log-density.

Helpers for parameterized distributions and kernels.

val dist0 : (RNG.t -> 'a) -> ('a -> Log_space.t) -> 'a t

val dist1 : ('a -> RNG.t -> 'b) -> ('a -> 'b -> Log_space.t) -> 'a -> 'b t

val dist2 : 
  ('a -> 'b -> RNG.t -> 'c) ->
  ('a -> 'b -> 'c -> Log_space.t) ->
  'a ->
  'b ->
  'c t

val kernel1 : 
  ('a -> 'b -> RNG.t -> 'b) ->
  ('a -> 'b -> 'b -> Log_space.t) ->
  'b ->
  'a ->
  'b t

Combinators on distributions and kernels.

val iid : int -> 'a t -> 'a array t

iid n dist constructs the n-fold iid distribution where each component is distributed according to dist.

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

conv f g dist transports a distribution along a pair of maps f,g. f,g must both be total. They need not be bijections, though not respecting this additional constraint might cause unspecified behaviours for some inference algorithms..