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Document type:
Masterarbeit
Author(s):
Brian Zeiser Dietrich
Title:
Y-Vine Copula Based Structure Learning for Continuous Bayesian Networks
Abstract:
Bayesian networks are powerful graphical models that capture the probabilistic dependencies between random variables. Pair-copula Bayesian networks (Bauer et al. 2011) extend the well-known Gaussian Bayesian networks by allowing for non-Gaussian distributions through the incorporation of bivariate copulas and univariate marginal distributions into the network structure. A widely used method for learning the structure of Bayesian networks is the constraint-based PC algorithm (Spirtes et al. 1993)...     »
Subject:
MAT Mathematik
DDC:
510 Mathematik
Supervisor:
Claudia Czado
Advisor:
Claudia Czado
Date of acceptation:
27.09.2024
Year:
2024
Quarter:
3. Quartal
Year / month:
2024-09
Month:
Sep
Pages:
141
Language:
en
University:
Technische Universität München
Faculty:
TUM School of Computation, Information and Technology
TUM Institution:
Professur für Angewandte Mathematische Statistik
Format:
Text
Presentation date:
16.10.2024
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