Inductive Inference 1.1 (+ case studies)

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Note that these working notes may reflect the development history of a piece of code as well as, or more than, the condition of the current version (which generally needs a good tidy up).

1: [Mixture of Markov models] -- unsupervised classification (clustering) of sequences.
2: [Bayesian Networks] -- of mixed, discrete & continuous, multivariate data.
3: [TimeSeries] -- by stateful functions.
4: [> 1 s].
5: [KL].
6: [CF].
7: [Iris data], unsupervised and supervised classification.
8: [Set-valued] variable (attribute, column).
9: [Segmentation] of a series.
[Missing data] -- estimator and model for Maybe ds.

Also see:
L. Allison,  Inductive Inference 1.1TR 2004/153, School of Computer Science & Software Engineering, Monash University, Australia 3800, May 2004,
L. Allison, Coding Ockham's Razor, Springer, doi:10.1007/978-3-319-76433-7, 2018.
and more [references] including TR 2005/177 'Inductive inference 1.1.2'.
Coding Ockham's Razor, L. Allison, Springer

A Practical Introduction to Denotational Semantics, L. Allison, CUP

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© L. Allison   (or as otherwise indicated),
Faculty of Information Technology (Clayton), Monash University, Australia 3800 (6/'05 was School of Computer Science and Software Engineering, Fac. Info. Tech., Monash University,
was Department of Computer Science, Fac. Comp. & Info. Tech., '89 was Department of Computer Science, Fac. Sci., '68-'71 was Department of Information Science, Fac. Sci.)
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