<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:creator>Piatti, Alberto</dc:creator>
  <dc:creator>Zaffalon, Marco</dc:creator>
  <dc:creator>Trojani, Fabio</dc:creator>
  <dc:creator>Hutter, Marcus</dc:creator>
  <dc:date>2009</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">In this paper, we consider the coherent theory of (epistemic) uncertainty  of Walley, in which beliefs are represented through sets of probability  distributions, and we focus on the problem of modeling prior ignorance  about a categorical random variable. In this setting, it is a known result  that a state of prior ignorance is not compatible with learning. To  overcome this problem, another state of beliefs, called near-ignorance,  has been proposed. Near-ignorance resembles ignorance very closely,  by satisfying some principles that can arguably be regarded as  necessary in a state of ignorance, and allows learning to take place.  What this paper 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, and 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.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://n2t.net/ark:/12658/srd1318435</dc:identifier>
  <dc:identifier>https://susi.usi.ch/global/documents/318435</dc:identifier>
  <dc:identifier>https://susi.usi.ch/documents/318435/files/trojani_IJAR_2009.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijar.2008.08.003</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/ark/12658/srd1318435</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:source>International journal of approximate reasoning. - Elsevier. - 2009, vol. 50, no. 4, p. 597-611</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Near-ignorance set of priors</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">latent variables</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">imprecise Dirichlet model</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/33</dc:subject>
  <dc:title xmlns:ns4="xml" ns4:lang="en">Limits of learning about a categorical latent variable under prior near-ignorance</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
