Coding Ockham's Razor

See: L. Allison, Coding Ockham's Razor', Springer, isbn13: 978-3319764320, doi:10.1007/978-3-319-76433-7, 2018.
1 Introduction doi:10.1007/978-3-319-76433-7_1 Information, Bayes, Shannon, MML 2 Discrete doi:10.1007/978-3-319-76433-7_2 Categorical, Bounded, Two-state, Multi-state, Adaptive 3 Integers doi:10.1007/978-3-319-76433-7_3 Geometric, Poisson, Universal, log^*, W.tree, distributions and codes 4 Continuous doi:10.1007/978-3-319-76433-7_4 ByPdf, Exponential, Normal (Gaussian), Laplace, ... 5 Function Models doi:10.1007/978-3-319-76433-7_5 regressions, Intervals, CPT 6 Multivariate doi:10.1007/978-3-319-76433-7_6 tuples, Independent, Dependent 7 Mixture Models doi:10.1007/978-3-319-76433-7_7 Mixtures, unsupervised classification, clustering, 27-club (musicians) 8 Function Models 2 doi:10.1007/978-3-319-76433-7_8 Naive Bayes, classification-/ decision-/ regression-Trees 9 Vectors doi:10.1007/978-3-319-76433-7_9 R^D, Simplex, Dirichlet, Direction, vMF (von Mises Fisher) 10 Linear Regression doi:10.1007/978-3-319-76433-7_10 11 Graphs doi:10.1007/978-3-319-76433-7_11 Graphs (networks), GER (Gilbert, Erdos, Renyi), Skewed, Motifs 12 Bits and Pieces doi:10.1007/978-3-319-76433-7_12 getting started, pitfalls, hints, testing,... 13 An Implementation doi:10.1007/978-3-319-76433-7_13 code, values, utilities, λ,...

doc/: online documentation
release.tar.gz (experimental): and
release-2018-05-13.tar.gz contain ...: classes/ — Java classes compiled from src/; data/ — some example data-sets; doc/ — documentation for Java source code; src/ — Java source code; LICENCE.txt

Lloyd Allison, 'Subclasses of Class Function used to Implement Transformations of Statistical Models', doi:10.48550/arXiv.2207.04218, July 2022.
Lloyd Allison, Arun S. Konagurthu and Daniel F. Schmidt, 'On Universal Codes for Integers: Wallace Tree, Elias Omega and Beyond', Data Compression Conference (DCC), pp.313-322, doi:10.1109/DCC50243.2021.00039, 22-26 March 2021.
Lloyd Allison, 'Coding Ockham's Razor', Springer, isbn13: 978-3319764320, doi:10.1007/978-3-319-76433-7, 2018.

Also see publications and other MML.

1	Introduction	doi:10.1007/978-3-319-76433-7_1	Information, Bayes, Shannon, MML
2	Discrete	doi:10.1007/978-3-319-76433-7_2	Categorical, Bounded, Two-state, Multi-state, Adaptive
3	Integers	doi:10.1007/978-3-319-76433-7_3	Geometric, Poisson, Universal, log^*, W.tree, distributions and codes
4	Continuous	doi:10.1007/978-3-319-76433-7_4	ByPdf, Exponential, Normal (Gaussian), Laplace, ...
5	Function Models	doi:10.1007/978-3-319-76433-7_5	regressions, Intervals, CPT
6	Multivariate	doi:10.1007/978-3-319-76433-7_6	tuples, Independent, Dependent
7	Mixture Models	doi:10.1007/978-3-319-76433-7_7	Mixtures, unsupervised classification, clustering, 27-club (musicians)
8	Function Models 2	doi:10.1007/978-3-319-76433-7_8	Naive Bayes, classification-/ decision-/ regression-Trees
9	Vectors	doi:10.1007/978-3-319-76433-7_9	R^D, Simplex, Dirichlet, Direction, vMF (von Mises Fisher)
10	Linear Regression	doi:10.1007/978-3-319-76433-7_10
11	Graphs	doi:10.1007/978-3-319-76433-7_11	Graphs (networks), GER (Gilbert, Erdos, Renyi), Skewed, Motifs
12	Bits and Pieces	doi:10.1007/978-3-319-76433-7_12	getting started, pitfalls, hints, testing,...
13	An Implementation	doi:10.1007/978-3-319-76433-7_13	code, values, utilities, λ,...