Learning about a Categorical Latent Variable under Prior Near-Ignorance
| dc.creator | Piatti, Alberto | |
| dc.creator | Zaffalon, Marco | |
| dc.creator | Trojani, Fabio | |
| dc.creator | Hutter, Marcus | |
| dc.date | 2007-05-29 | |
| dc.date.accessioned | 2026-07-07T09:46:28Z | |
| dc.date.available | 2026-07-07T09:46:28Z | |
| dc.description | It is well known that complete prior ignorance is not compatible with learning, at least in a coherent theory of (epistemic) uncertainty. What is less widely known, is that there is a state similar to full ignorance, that Walley calls near-ignorance, that permits learning to take place. In this paper we provide new and substantial evidence that also near-ignorance cannot be really regarded as a way out of the problem of starting statistical inference in conditions of very weak beliefs. The key to this result is focusing on a setting characterized by a variable of interest that is latent. We argue that such a setting is by far the most common case in practice, and we show, for the case of categorical latent variables (and general manifest variables) that there is a sufficient condition that, if satisfied, prevents learning to take place under prior near-ignorance. This condition is shown to be easily satisfied in the most common statistical problems. | |
| dc.description | 15 LaTeX pages | |
| dc.identifier | https://arxiv.org/abs/0705.4312 | |
| dc.identifier | http://arxiv.org/abs/0705.4312 | |
| dc.identifier | Proc. 5th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA 2007) pages 357-364 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/163542 | |
| dc.subject | Probability | |
| dc.subject | Statistics Theory | |
| dc.title | Learning about a Categorical Latent Variable under Prior Near-Ignorance | |
| dc.type | text |