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Uncertainty modelling in data science
~
Destercke, Sebastien.
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Uncertainty modelling in data science
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Uncertainty modelling in data science/ edited by Sebastien Destercke ... [et al.].
其他作者:
Destercke, Sebastien.
出版者:
Cham :Springer International Publishing : : 2019.,
面頁冊數:
xi, 234 p. :ill., digital ;24 cm.
內容註:
Chapter 1. Imprecise statistical inference for accelerated life testing data: imprecision related to the log-rank test (Abdullah Ahmadini) -- Chapter 2. Descriptive comparison of the rating scales through different scale estimates. Simulation-based analysis (Irene Arellano) -- Chapter 3. Central Moments of a Fuzzy Random Variable using the Signed Distance: a Look towards the Variance (Redina Berkachy) -- Chapter 4. On Missing Membership Degrees: Modelling Non-existence, Ignorance and Inconsistency (Michal Burda) etc.
Contained By:
Springer eBooks
標題:
Big data. -
電子資源:
http://dx.doi.org/10.1007/978-3-319-97547-4
ISBN:
9783319975474
Uncertainty modelling in data science
Uncertainty modelling in data science
[electronic resource] /edited by Sebastien Destercke ... [et al.]. - Cham :Springer International Publishing :2019. - xi, 234 p. :ill., digital ;24 cm. - Advances in intelligent systems and computing,v.8322194-5357 ;. - Advances in intelligent systems and computing ;v.832..
Chapter 1. Imprecise statistical inference for accelerated life testing data: imprecision related to the log-rank test (Abdullah Ahmadini) -- Chapter 2. Descriptive comparison of the rating scales through different scale estimates. Simulation-based analysis (Irene Arellano) -- Chapter 3. Central Moments of a Fuzzy Random Variable using the Signed Distance: a Look towards the Variance (Redina Berkachy) -- Chapter 4. On Missing Membership Degrees: Modelling Non-existence, Ignorance and Inconsistency (Michal Burda) etc.
ISBN: 9783319975474
Standard No.: 10.1007/978-3-319-97547-4doiSubjects--Topical Terms:
2045508
Big data.
LC Class. No.: QA76.9.B45 / U53 2019
Dewey Class. No.: 005.7
Uncertainty modelling in data science
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