mml

Class README

java.lang.Object

mml.README

public class README
extends java.lang.Object

About package 'mml' — tools for Minimum Message Length inference.

• MML — core constants, functions and Models, including
  • Known, Geometric0, logStar0, WallaceInt0, Poisson0, Normal (Gaussian), N01 (μ=0, σ=1), Laplace, vMF3 (von Mises Fisher in 3-D), etc
• UPModel — unparameterised statistical Models
• Model — fully parameterised statistical Models (also UPModel.M)
• Estimator — estimate a fully parameterised statistical Model (also UPModel.Est)
• UPFunctionModel — unparameterised Function-Models
• FunctionModel — fully parameterised Function-Models (also UPFunctionModel.M)
• UPSeriesModel — unparameterised Series-Models
• SeriesModel — fully parameterised Series-Models (also UPSeriesModel.M)
• etc
  
  
• Discretes — models of discrete data
• MultiState — models of Bernoulli trials – coins, dice and the like
• Continuous — models of continuous, R, data
• R_D — models of multivariate continuous, R^D, data
• Multinomial
• CPT — Conditional Probability Tables
• Multivariate
• Intervals — discretization
• NaiveBayes — naive Bayes
• Mixture — Mixture Models
• Tree — decision- (classification-, regression-) Trees
• Graphs — Models of Graphs (networks)
• UPModel.Transform — Transformed Models, e.g., log-Normal
• etc

Also see supporting package(s) [la].

References:

• L. Allison, 'Coding Ockham's Razor', Springer, 2018, isbn13: 978-3319764320, [doi:10.1007/978-3-319-76433-7]
• L. Allison, 'Models for machine learning and data mining in functional programming,' Journal of Functional Programming (JFP), 15(1), pp.15-32, January 2005, [doi:10.1017/S0956796804005301]
• C. S. Wallace 'Statistical and Inductive Inference by Minimum Message Length,' Springer (Information Science and Statistics), pp.432, May 2005, [doi:10.1007/0-387-27656-4]
• C. S. Wallace & D. M. Boulton, 'An information measure for classification,' Computer Journal, 11(2), pp.185-194, August 1968, [doi:10.1093/comjnl/11.2.185]
• Also see the [home page] www.cantab.net/users/mmlist/MML/A/ for source-code releases.

On Big Data: Note that MML is agnostic about big v small data; if you have an algorithm to efficiently calculate, or approximate, the sufficient statistics of a model and to calculate a valid message length for a huge data-set, MML is amenable to that.

Copyright © 2018 Lloyd Allison. Reference: Lloyd Allison, "Coding Ockham's Razor", Springer, 2018. This file is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License (GPL) as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This file is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this file. If not, see http://www.gnu.org/licenses/

Constructor Summary

Constructors

Constructor and Description
README()

Method Summary

Modifier and Type	Method and Description
java.lang.String	toString()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

README

public README()

Method Detail

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object