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Probability Model Type Sufficiency Leigh J. Fitzgibbon, Lloyd Allison and Joshua W. Comley
 

Summary: Probability Model Type Sufficiency
Leigh J. Fitzgibbon, Lloyd Allison and Joshua W. Comley
School of Computer Science and Software Engineering
Monash University, Victoria 3800, Australia
{leighf,lloyd,joshc}@bruce.csse.monash.edu.au
Abstract. We investigate the role of sufficient statistics in generalized
probabilistic data mining and machine learning software frameworks.
Some issues involved in the specification of a statistical model type are
discussed and we show that it is beneficial to explicitly include a suf-
ficient statistic and functions for its manipulation in the model type's
specification. Instances of such types can then be used by generalized
learning algorithms while maintaining optimal learning time complex-
ity. Examples are given for problems such as incremental learning and
data partitioning problems (e.g. change-point problems, decision trees
and mixture models).
1 Introduction
The formal specification of a statistical model type is an important ingredient
of machine learning software frameworks [1]. In the interests of software reuse,
robustness, and applicability the model type should encompass a general notion
of a statistical model, and allow generalized machine learning algorithms to op-

  

Source: Allison, Lloyd - Caulfield School of Information Technology, Monash University

 

Collections: Computer Technologies and Information Sciences