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Combining models in appropriate ways to achieve high performance is commonlyseen in machine learning fields today. Although a large amount of combinatorialmodels have been created, little attention is drawn to the commons in differentmodels and their connections. A general modelling technique is thus worthstudying to understand model combination deeply and shed light on creating newmodels. Prediction markets show a promise of becoming such a generic, flexiblecombinatorial model. By reviewing on several popular combinatorial models andprediction market models, this paper aims to show how the market models cangeneralise different combinatorial stuctures and how they implement thesepopular combinatorial models in specific conditions. Besides, we will see amongdifferent market models, Storkey's \emph{Machine Learning Markets} provide morefundamental, generic modelling mechanisms than the others, and it has asignificant appeal for both theoretical study and application.

Combinatorial Modelling and Learning with Prediction Markets