## Structured Hypotheses

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.