[II/ver.1.1/]   <ver.1.0/TO-DO.html<

200312, ver.1.1, to do

  1. Probably a better standard to estimate a time-series model from a set of data series, [[dataSpace]], rather than from just one, [dataSpace].
  2. It might be better to deal with "weighted data" via a  data Weighted d = Wt Double d  (although it is estimators that work on weighted data - does one really have a Model of weighted data?),
    as inspired by the success of treating missing data, Maybe d, and associated models and operators in the [Bayes-nets] case study.
    (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?
  3. 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 roughly estimator ds -> estWeighted ds. The ss must be additive, scalable?
  4. Generalize the current 0-lookahead search for classification-trees to n-lookahead, n>0.
  5. Add splitting and merging of components to the mixture-model search.
  6. Provide some simple I/O support, e.g. for `comma separated variable' files.
  7. 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.
  8. ...

5/11/2004,... 2005,..., L. Allison, School of Computer Science and Software Engineering, Monash University, Australia 3800.
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