TY - JOUR
T1 - Learning imprecise probability models: Conceptual and practical challenges
AU - Cozman, Fabio Gagliardi
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.ijar.2014.04.016
DO - 10.1016/j.ijar.2014.04.016
M3 - Note
SP - 1594
EP - 1596
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
SN - 0888-613X
ER -