package prbnmcn-stats

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Introduction

prbnmcn-stats
- Stats
  - Binning
  - Combi
  - Emp
    
    Empirical_statistics
    
    Float
    
    Rational
  - Fin
    
    Float
    
    Dist
    
    Rational
    
    Dist
  - Finbij
    
    Make
    
    Elt
    
    S
  - Float_table
  - Gen
    
    Infix
    
    Make
    
    Infix
    
    RNG
    
    Rational
    
    Rational
  - Graph
    
    Dist
    
    Graph_statistics
    
    Table
    
    Tree
    
    Undirected_edge
    
    Vertex_bij
    
    Vertex_set
    
    Vertex_table
    
    Make
    
    Graph
    
    V
    
    Table
    
    Tree
    
    Undirected_edge
    
    Vertex_bij
    
    Vertex_set
    
    Vertex_table
  - Int_table
  - Log_space
  - Mh
    
    MH_parameters
    
    Make
    
    X
    
    Make_core_sampling_loop
    
    X
  - Pdfs
  - Specfun
  - Stats_intf
    
    Fin_dist
    
    Gen
    
    Infix
    
    Rational
    
    Graph
    
    V
    
    Stateful_PRNG
  - String_table

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The prbnmcn-stats statistical library.

Introduction

prbnmcn-stats (stats for short) was designed to construct simple probabilistic models and compute basic statistics over empirical data obtained from various experiments.

The core data type is that of a probability distribution. Distributions come under various guises.

Empirical distributions are the typical outcome of performing a bunch of measurements or experiments, concretely they are nothing more than a collection of data. The empirical mean, empirical variance and quantiles are typical statistics that can be computed on empirical distributions.
Generative distributions a.k.a. samplers are used when constructing probabilistic models, they are typically used to generate synthetic data.
Measures are the mathematical representation of a distribution, associating weights to (sets of) data. This is the form we use to compute statistics such as the mean, the variance, etc of a distribution. Typically, we manipulate only finitely supported measures.

stats provides modules dedicated for each case, and maps between the various forms under which probability distributions appear.

module Stats_intf : sig ... end

Type signatures.

module Fin : sig ... end

Finite measures implemented as finitely supported functions.

module Emp : sig ... end

Empirical distributions.

module Gen : sig ... end

Generative distributions.

module Pdfs : sig ... end

Probability density functions (pdfs).

module Combi : sig ... end

Simple combinatorics.

module Mh : sig ... end

Metropolis-Hastings MCMC

module Finbij : sig ... end

Bijections between finite sets and {0; ...; n-1}.

module Graph : sig ... end

Basic statistics on graphs (experimental)

module Log_space : sig ... end

Log-space computation

module Binning : sig ... end

Binning finitely supported distributions

module Specfun : sig ... end

module Int_table : sig ... end

module String_table : sig ... end

module Float_table : sig ... end

val int_table : (module Hashtbl.S with type key = int)

val string_table : (module Hashtbl.S with type key = string)

val float_table : (module Hashtbl.S with type key = float)