TY - GEN A1 - Golas, Ulrike T1 - A General Attribution Concept for Models in M- adhesive Transformation Systems: Long Version N2 - Attributes are an important concept for modeling data in practical applications. Up to now there is no adequate way to define attributes for different kinds of models used in M-adhesive transformation systems, which are a special kind of graph transformation systems based on M-adhesive categories. Especially a proper representation and definition of attributes and their values as well as a suitable handling of the data does not fit well with other graph transformation formalisms. In this paper, we propose a new method to define attributes in a natural, but still formally precise and widely applicable way. We define a new kind of adhesive category, called W-adhesive, that can be used for transformations of attributes, while the underlying models are still M-adhesive ones. As a result, attributed models can be used as they are intended to be, but with a formal background and proven well-behavior. T3 - ZIB-Report - 12-22 KW - graph transformation KW - attribution KW - adhesive categories Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-15547 SN - 1438-0064 ER - TY - GEN A1 - Klebanov, Ilja A1 - Sikorski, Alexander A1 - Schütte, Christof A1 - Röblitz, Susanna T1 - Empirical Bayes Methods, Reference Priors, Cross Entropy and the EM Algorithm N2 - When estimating a probability density within the empirical Bayes framework, the non-parametric maximum likelihood estimate (NPMLE) usually tends to overfit the data. This issue is usually taken care of by regularization - a penalization term is subtracted from the marginal log-likelihood before the maximization step, so that the estimate favors smooth solutions, resulting in the so-called maximum penalized likelihood estimation (MPLE). The majority of penalizations currently in use are rather arbitrary brute-force solutions, which lack invariance under transformation of the parameters(reparametrization) and measurements. This contradicts the principle that, if the underlying model has several equivalent formulations, the methods of inductive inference should lead to consistent results. Motivated by this principle and using an information-theoretic point of view, we suggest an entropy-based penalization term that guarantees this kind of invariance. The resulting density estimate can be seen as a generalization of reference priors. Using the reference prior as a hyperprior, on the other hand, is argued to be a poor choice for regularization. We also present an insightful connection between the NPMLE, the cross entropy and the principle of minimum discrimination information suggesting another method of inference that contains the doubly-smoothed maximum likelihood estimation as a special case. T3 - ZIB-Report - 16-56 KW - parameter estimation KW - Bayesian inference KW - Bayesian hierarchical modeling KW - hyperparameter KW - hyperprior KW - EM algorithm KW - NPMLE KW - MPLE KW - DS-MLE KW - principle of maximum entropy KW - cross entropy KW - minimum discrimination information KW - reference prior KW - Jeffreys prior Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-61230 SN - 1438-0064 ER - TY - GEN A1 - Becker, Kai-Helge A1 - Hiller, Benjamin T1 - Improved optimization models for potential-driven network flow problems via ASTS orientations N2 - The class of potential-driven network flow problems provides important models for a range of infrastructure networks that lead to hard-to-solve MINLPs in real-world applications. On large-scale meshed networks the relaxations usually employed are rather weak due to cycles in the network. To address this situation, we introduce the concept of ASTS orientations, a generalization of bipolar orientations, as a combinatorial relaxation of feasible solutions of potential-driven flow problems, study their structure, and show how they can be used to strengthen existing relaxations and thus provide improved optimization models. Our computational results indicate that ASTS orientations can be used to derive much stronger bounds on the flow variables than existing bound tightening methods and to yield significant performance improvements for an existing state-of-the-art MILP model for large-scale gas networks. T3 - ZIB-Report - 19-58 KW - potential-driven network flows KW - mixed-integer nonlinear programming KW - ASTS orientations KW - bipolar orientations Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-75347 SN - 1438-0064 ER - TY - GEN A1 - Taentzer, Gabriele A1 - Golas, Ulrike T1 - Local Confluence Analysis for Amalgamated Graph Transformation: Long Version N2 - Amalgamated graph transformation allows to define schemes of rules coinciding in common core activities and differing over additional parallel independent activities. Consequently, a rule scheme is specified by a kernel rule and a set of extending multi-rules forming an interaction scheme. Amalgamated transformations have been increasingly used in various modeling contexts. Critical Pair Analysis (CPA) can be used to show local confluence of graph transformation systems. It is an open challenge to lift the CPA to amalgamated graph transformation systems, especially since infinite many pairs of amalgamated rules occur in general. As a first step towards an efficient local confluence analysis of amalgamated graph transformation systems, we show that the analysis of a finite set of critical pairs suffices to prove local confluence. T3 - ZIB-Report - 15-29 KW - amalgamated graph transformation KW - parallel independence KW - critical pair analysis Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-54943 SN - 1438-0064 ER -