Abstract
We suggest to define objective probabilities by similarity-weighted empirical frequencies, where more similar cases get a higher weight in the computation of frequencies. This formula is justified intuitively and axiomatically, but raises the question, which similarity function should be used? We propose to estimate the similarity function from the data, and thus obtain objective probabilities. We compare this definition to others, and attempt to delineate the scope of situations in which objective probabilities can be used.
| Original language | English |
|---|---|
| Title of host publication | Case-Based Predictions |
| Subtitle of host publication | An Axiomatic Approach to Prediction, Classification and Statistical Learning |
| Publisher | Taylor and Francis |
| Pages | 259-280 |
| Number of pages | 22 |
| ISBN (Electronic) | 9789814366182 |
| ISBN (Print) | 981436617X, 9789814366175 |
| DOIs | |
| State | Published - 1 Jan 2012 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2012 by World Scientific Publishing Co. Pte. Ltd. All rights reserved.
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