<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Trojani, Fabio</dc:contributor>
  <dc:creator>Piatti, Alberto</dc:creator>
  <dc:date>2006-10-19</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">It is well known that a state of 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 another state of beliefs, called near-ignorance, that resembles ignorance very closely by satisfying some principles that can arguably be regarded as necessary in a state of ignorance, and that allows learning to take place. What this thesis does is to 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. In the first part of the thesis we provide, for the case of categorical latent variables (and general manifest variables) a condition that, if satisfied, prevents learning to take place under prior near-ignorance. This condition is shown to be easily satisfied even in the most common statistical problems. We regard these results as a strong form of evidence against the possibility to adopt a condition of prior near-ignorance in real statistical problems. In the second part of the thesis we propose a slightly modified framework that allows learning to take place under a very weak specification of prior knowledge. The proposed approach is a first preliminary attempt to reconcile latent variables with a very weak specification of prior knowledge.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://localhost:5000/ark:/12658/srd1318179</dc:identifier>
  <dc:identifier>https://susi.usi.ch/global/documents/318179</dc:identifier>
  <dc:identifier>https://susi.usi.ch/documents/318179/files/2006ECO004.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:rero-006-108300</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/ark/12658/srd1318179</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Prior near ignorance</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Latent variables</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">Learning</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/33</dc:subject>
  <dc:title xmlns:ns4="xml" ns4:lang="en">Learning under prior ignorance</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_db06</dc:type>
</oai_dc:dc>
