Learning imprecise probability models: Conceptual and practical challenges

Fabio Gagliardi Cozman

Research output: Contribution to journalNotepeer-review

2 Scopus citations

Abstract

The paper by Masegosa and Moral, on "Imprecise probability models for learning multinomial distributions from data", considers the combination of observed data and minimal prior assumptions so as to produce possibly interval-valued parameter estimates. We offer an evaluation of Masegosa and Moral's proposals. © 2014 Elsevier Inc.
Original languageAmerican English
Pages (from-to)1594-1596
Number of pages3
JournalInternational Journal of Approximate Reasoning
DOIs
StatePublished - 1 Jan 2014
Externally publishedYes

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