^MML^
^Structured^

# Estimation and Inference by Compact Coding

### C. S. Wallace, P. R. Freeman

### Journal of the Royal Statistical Society.
Series B (Methodological),
Vol. 49,
No. 3.
(1987),
pp. 240-265.

Paper is
[here (click)]
at Jstor [4/'01].

**Summary**:
The systematic variation within a set of data,
as represented by a usual statistical model,
may be used to encode the data in a more compact form
than would be possible if they were considered to be purely random.
The encoded form has two parts.
The first states the inferred estimates of the unknown parameters
in the model,
the second states the data using an optimal code based on the
data probability distribution implied by those parameter estimates.
Choosing the model and the estimates that give the most compact
coding leads to an interesting general inference procedure.
In its strict form it has great generality and
several nice properties but is computationally infeasible.
An approximate form is developed and its relation to other methods is explored.

L. Allison,
School of Computer Science and Software Engineering,
Monash University, Australia 3168.
Created with "vi (Linux + IRIX)", charset=iso-8859-1