Structured Hypotheses

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We have seen the Minimum Message Length (MML) and other treatments of "simple" hypotheses over simple data such as finite choices, e.g., multi-state, and continuous values, e.g., normal distribution, etc.. It turns out that structured hypotheses can be formed from combinations of simple hypotheses plus a component to describe the particular structure.

Structured Models
Sequences, time-series, grids, images, ...
Hidden Markov Models.
HMM & PFSA.
Segmentation.
Bioinformatics, DNA, proteins, genes, evolutionary trees.
Alignment and
Phylogenetic (Evolutionary) Trees.
Supervised Learning, expert systems, regressions, rule learning.
Decision-Trees (Classification-Trees), supervised classification, and
Decision-Graphs.
Bayesian Networks and Causal Models.
Mixed Bayes Nets -- discrete & continuous (& structured) variables.
Hybrid Models (local structure).
ANNs, Artificial Neural Networks.
Unsupervised Learning, clustering, Snob, numerical taxonomy, rule discovery.
Mixture Models, unsupervised classification, clustering,
in series,
Stats. & Comp..
Factor Analysis Models
Single Factor, &
Multiple Factors.
WB68, WF87.
Inductive Programming (IP)
ACSC03.
II 1.0.
JFP.
ACSC06.
ActaOe.
IP 1.2, TR 224.
Trees & Graphs.
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