- Probably a better standard to estimate a time-series model from a set of data series, [[dataSpace]], rather than from just one, [dataSpace].
- It
*might*be better to deal with "weighted data" via a (although it is**data**Weighted d = Wt Double d*estimators*that work on weighted data - does one really have a*Model*of weighted data?),

as inspired by the success of treating missing data, , and associated models and operators in the [Bayes-nets] case study.`Maybe d`

(It might be worth treating continuous data (measurement accuracy) in a similar way, or not?-)

Carefully consider [__missing data__],__weighted data__, and__measurement accuracy__of continuous data. Weighted data are primarily needed for [mixture modelling] -- fractional weights for class memberships (and can also represent repeated values). But being missing is like having a weight of zero, and also like having vanishingly low measurement accuracy. Should measurement accuracy be an explicit component of every continuous datum? - If so, sufficient statistics be manipulated
as a single tuple by uncurried functions?
Then such functions could have the same (polymorphic) type,
and composition of the counter-function and the model-builder
would be slightly easier.

Under what conditions is there an operator of type roughlyestimator ds -> estWeighted ds. The ss must be additive, scalable? - Generalize the current 0-lookahead search for classification-trees to n-lookahead, n>0.
- Add splitting and merging of components to the mixture-model search.
- Provide some simple I/O support, e.g. for `comma separated variable' files.
- Always: Look for places to make better use of the prelude functions, e.g. any, all, fold[l|r], min, max, repeat, scan[l|r], sum, zipWith, etc., to simplify the code.
`...`

5/11/2004,... 2005,..., L. Allison, School of Computer Science and Software Engineering, Monash University, Australia 3800. Created with "vi (Linux & Solaris)", charset=iso-8859-1