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, Causal Models, Graphical Models, ...

Mixed Bayes Nets -- discrete & continuous (& structured) variables.

Hybrid Models (local structure).

Log-linear analysis, Chordalysis MML

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.