On assigning probabilities to new hypotheses

The paper proposes the way how to assign a proper prior probability to a new, generally compound, hypothesis. To this purpose, it uses the minimum relative-entropy principle and a forecaster-based knowledge transfer. Methodologically, it opens a way towards enriching the standard Bayesian framework by the possibility to extend the set of models during learning without the need to restart. The presented use scenarios concern: (a) creating new hypotheses, (b) learning problems with an insufficient amount of data, and (c) sequential Monte Carlo estimation. They indicate a strong application potential of the proposed technique. Related interesting open research problems are listed. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The paper presents a method for assigning proper prior probabilities to new, generally compound hypotheses using the minimum relative-entropy principle and forecaster-based knowledge transfer. The technique shows strong application potential in creating hypotheses, addressing learning problems with insufficient data, and sequential Monte Carlo estimation. Interesting open research problems related to this method are also listed.