Standard
Statistical Models for Partial Orders Based on Data Depth and Formal Concept Analysis. / Blocher, Hannah; Schollmeyer, Georg
; Jansen, Christoph.
Information Processing and Management of Uncertainty in Knowledge-Based Systems. ed. / Davide Ciucci; Inés Couso; Jesús Medina; Dominik Ślęzak; Davide Petturiti; Bernadette Bouchon-Meunier; Ronald R. Yager. Cham: Springer, 2022. (Communications in Computer and Information Sciences; Vol. 1602).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Blocher, H, Schollmeyer, G
& Jansen, C 2022,
Statistical Models for Partial Orders Based on Data Depth and Formal Concept Analysis. in D Ciucci, I Couso, J Medina, D Ślęzak, D Petturiti, B Bouchon-Meunier & RR Yager (eds),
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Communications in Computer and Information Sciences, vol. 1602, Springer, Cham.
https://doi.org/10.1007/978-3-031-08974-9_2APA
Blocher, H., Schollmeyer, G.
, & Jansen, C. (2022).
Statistical Models for Partial Orders Based on Data Depth and Formal Concept Analysis. In D. Ciucci, I. Couso, J. Medina, D. Ślęzak, D. Petturiti, B. Bouchon-Meunier, & R. R. Yager (Eds.),
Information Processing and Management of Uncertainty in Knowledge-Based Systems (Communications in Computer and Information Sciences; Vol. 1602). Springer.
https://doi.org/10.1007/978-3-031-08974-9_2Vancouver
Blocher H, Schollmeyer G
, Jansen C.
Statistical Models for Partial Orders Based on Data Depth and Formal Concept Analysis. In Ciucci D, Couso I, Medina J, Ślęzak D, Petturiti D, Bouchon-Meunier B, Yager RR, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cham: Springer. 2022. (Communications in Computer and Information Sciences). doi: 10.1007/978-3-031-08974-9_2
Author
Bibtex
@inproceedings{9cb214ccaa5f461396df657e86ee642e,
title = "Statistical Models for Partial Orders Based on Data Depth and Formal Concept Analysis",
abstract = "In this paper, we develop statistical models for partial orders where the partially ordered character cannot be interpreted as stemming from the non-observation of data. After discussing some shortcomings of distance based models in this context, we introduce statistical models for partial orders based on the notion of data depth. Here we use the rich vocabulary of formal concept analysis to utilize the notion of data depth for the case of partial orders data. After giving a concise definition of unimodal distributions and unimodal statistical models of partial orders, we present an algorithm for efficiently sampling from unimodal models as well as from arbitrary models based on data depth.",
author = "Hannah Blocher and Georg Schollmeyer and Christoph Jansen",
year = "2022",
month = jul,
day = "4",
doi = "10.1007/978-3-031-08974-9_2",
language = "English",
isbn = "9783031089732",
series = "Communications in Computer and Information Sciences",
publisher = "Springer",
editor = "Davide Ciucci and Couso, {In{\'e}s } and Jes{\'u}s Medina and Dominik {\'S}l{\c e}zak and Petturiti, {Davide } and Bernadette Bouchon-Meunier and Yager, {Ronald R. }",
booktitle = "Information Processing and Management of Uncertainty in Knowledge-Based Systems",
}
RIS
TY - GEN
T1 - Statistical Models for Partial Orders Based on Data Depth and Formal Concept Analysis
AU - Blocher, Hannah
AU - Schollmeyer, Georg
AU - Jansen, Christoph
PY - 2022/7/4
Y1 - 2022/7/4
N2 - In this paper, we develop statistical models for partial orders where the partially ordered character cannot be interpreted as stemming from the non-observation of data. After discussing some shortcomings of distance based models in this context, we introduce statistical models for partial orders based on the notion of data depth. Here we use the rich vocabulary of formal concept analysis to utilize the notion of data depth for the case of partial orders data. After giving a concise definition of unimodal distributions and unimodal statistical models of partial orders, we present an algorithm for efficiently sampling from unimodal models as well as from arbitrary models based on data depth.
AB - In this paper, we develop statistical models for partial orders where the partially ordered character cannot be interpreted as stemming from the non-observation of data. After discussing some shortcomings of distance based models in this context, we introduce statistical models for partial orders based on the notion of data depth. Here we use the rich vocabulary of formal concept analysis to utilize the notion of data depth for the case of partial orders data. After giving a concise definition of unimodal distributions and unimodal statistical models of partial orders, we present an algorithm for efficiently sampling from unimodal models as well as from arbitrary models based on data depth.
U2 - 10.1007/978-3-031-08974-9_2
DO - 10.1007/978-3-031-08974-9_2
M3 - Conference contribution/Paper
SN - 9783031089732
T3 - Communications in Computer and Information Sciences
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems
A2 - Ciucci, Davide
A2 - Couso, Inés
A2 - Medina, Jesús
A2 - Ślęzak, Dominik
A2 - Petturiti, Davide
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
PB - Springer
CY - Cham
ER -
