TY - JOUR A1 - Klebanov, Ilja A1 - Sikorski, Alexander A1 - Schütte, Christof A1 - Röblitz, Susanna T1 - Objective priors in the empirical Bayes framework JF - Scandinavian Journal of Statistics N2 - When dealing with Bayesian inference the choice of the prior often remains a debatable question. Empirical Bayes methods offer a data-driven solution to this problem by estimating the prior itself from an ensemble of data. In the nonparametric case, the maximum likelihood estimate is known to overfit the data, an issue that is commonly tackled by regularization. However, the majority of regularizations are ad hoc choices which lack invariance under reparametrization of the model and result in inconsistent estimates for equivalent models. We introduce a nonparametric, transformation-invariant estimator for the prior distribution. Being defined in terms of the missing information similar to the reference prior, it can be seen as an extension of the latter to the data-driven setting. This implies a natural interpretation as a trade-off between choosing the least informative prior and incorporating the information provided by the data, a symbiosis between the objective and empirical Bayes methodologies. Y1 - 2021 U6 - https://doi.org/10.1111/sjos.12485 VL - 48 IS - 4 SP - 1212 EP - 1233 PB - Wiley Online Library ER - TY - THES A1 - Sikorski, Alexander T1 - An Information-Theoretic Empirical Bayes Method and its Application to a Systems Biology Model N2 - This thesis covers the development and application of an empirical Bayes method to the problem of parameter estimation in systems biology. The goal was to provide a general and practical solution to the Bayesian inverse problem in the case of high dimensional parameter spaces making use of present cohort-data. We show that the maximum penalized likelihood estimator (MPLE) with information penalty is based on natural, information-theoretic considerations and admits the desirable property of transformation invariance. Due to its concavity, the objective function is computationally feasible and its mesh-free Monte-Carlo approximation enables its application to high-dimensional problems eluding the curse of dimensionality. We furthermore show how to apply the developed methods to a real world problem by the means of Markov chain Monte-Carlo sampling (MCMC), affirming its proficiency in a practical scenario. Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-65098 ER - TY - GEN A1 - Klebanov, Ilja A1 - Sikorski, Alexander A1 - Schütte, Christof A1 - Röblitz, Susanna T1 - Prior estimation and Bayesian inference from large cohort data sets N2 - One of the main goals of mathematical modelling in systems biology related to medical applications is to obtain patient-specific parameterisations and model predictions. In clinical practice, however, the number of available measurements for single patients is usually limited due to time and cost restrictions. This hampers the process of making patient-specific predictions about the outcome of a treatment. On the other hand, data are often available for many patients, in particular if extensive clinical studies have been performed. Using these population data, we propose an iterative algorithm for contructing an informative prior distribution, which then serves as the basis for computing patient-specific posteriors and obtaining individual predictions. We demonsrate the performance of our method by applying it to a low-dimensional parameter estimation problem in a toy model as well as to a high-dimensional ODE model of the human menstrual cycle, which represents a typical example from systems biology modelling. T3 - ZIB-Report - 16-09 Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-57475 SN - 1438-0064 ER - TY - GEN A1 - Klebanov, Ilja A1 - Sikorski, Alexander A1 - Schütte, Christof A1 - Röblitz, Susanna T1 - Empirical Bayes Methods for Prior Estimation in Systems Medicine N2 - One of the main goals of mathematical modelling in systems medicine related to medical applications is to obtain patient-specific parameterizations and model predictions. In clinical practice, however, the number of available measurements for single patients is usually limited due to time and cost restrictions. This hampers the process of making patient-specific predictions about the outcome of a treatment. On the other hand, data are often available for many patients, in particular if extensive clinical studies have been performed. Therefore, before applying Bayes’ rule separately to the data of each patient (which is typically performed using a non-informative prior), it is meaningful to use empirical Bayes methods in order to construct an informative prior from all available data. We compare the performance of four priors - a non-informative prior and priors chosen by nonparametric maximum likelihood estimation (NPMLE), by maximum penalized lilelihood estimation (MPLE) and by doubly-smoothed maximum likelihood estimation (DS-MLE) - by applying them to a low-dimensional parameter estimation problem in a toy model as well as to a high-dimensional ODE model of the human menstrual cycle, which represents a typical example from systems biology modelling. T3 - ZIB-Report - 16-57 KW - Parameter estimation KW - Bayesian inference KW - Bayesian hierarchical modelling KW - NPMLE KW - MPLE KW - DS-MLE KW - EM algorithm KW - Jeffreys prior KW - reference prior KW - hyperparameter KW - hyperprior KW - principle of maximum entropy Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-61307 SN - 1438-0064 ER - TY - THES A1 - Sikorski, Alexander T1 - PCCA+ and Its Application to Spatial Time Series Clustering Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-58769 ER - TY - JOUR A1 - Sikorski, Alexander A1 - Weber, Marcus A1 - Schütte, Christof T1 - The Augmented Jump Chain JF - Advanced Theory and Simulations N2 - Modern methods of simulating molecular systems are based on the mathematical theory of Markov operators with a focus on autonomous equilibrated systems. However, non-autonomous physical systems or non-autonomous simulation processes are becoming more and more important. A representation of non-autonomous Markov jump processes is presented as autonomous Markov chains on space-time. Augmenting the spatial information of the embedded Markov chain by the temporal information of the associated jump times, the so-called augmented jump chain is derived. The augmented jump chain inherits the sparseness of the infinitesimal generator of the original process and therefore provides a useful tool for studying time-dependent dynamics even in high dimensions. Furthermore, possible generalizations and applications to the computation of committor functions and coherent sets in the non-autonomous setting are discussed. After deriving the theoretical foundations, the concepts with a proof-of-concept Galerkin discretization of the transfer operator of the augmented jump chain applied to simple examples are illustrated. Y1 - 2021 U6 - https://doi.org/10.1002/adts.202000274 VL - 4 IS - 4 PB - Wiley-VCH 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 - JOUR A1 - Heida, Martin A1 - Sikorski, Alexander A1 - Weber, Marcus T1 - Consistency and order 1 convergence of cell-centered finite volume discretizations of degenerate elliptic problems in any space dimension JF - SIAM Journal on Numerical Analysis N2 - We study consistency of cell-centered finite difference methods for elliptic equations with degenerate coefficients in any space dimension $d \geq 2$. This results in order of convergence estimates in the natural weighted energy norm and in the weighted discrete $L^2$-norm on admissible meshes. The cells of meshes under consideration may be very irregular in size. We particularly allow the size of certain cells to remain bounded from below even in the asymptotic limit. For uniform meshes we show that the order of convergence is at least 1 in the energy semi-norm, provided the discrete and continuous solutions exist and the continuous solution has $H^2$ regularity. Y1 - 2022 U6 - https://doi.org/10.20347/WIAS.PREPRINT.2913 ER - TY - GEN A1 - Fackeldey, Konstantin A1 - Sikorski, Alexander A1 - Weber, Marcus T1 - Spectral Clustering for Non-reversible Markov Chains N2 - Spectral clustering methods are based on solving eigenvalue problems for the identification of clusters, e.g. the identification of metastable subsets of a Markov chain. Usually, real-valued eigenvectors are mandatory for this type of algorithms. The Perron Cluster Analysis (PCCA+) is a well-known spectral clustering method of Markov chains. It is applicable for reversible Markov chains, because reversibility implies a real-valued spectrum. We also extend this spectral clustering method to non-reversible Markov chains and give some illustrative examples. The main idea is to replace the eigenvalue problem by a real-valued Schur decomposition. By this extension non-reversible Markov chains can be analyzed. Furthermore, the chains do not need to have a positive stationary distribution. In addition to metastabilities, dominant cycles and sinks can also be identified. This novel method is called GenPCCA (i.e. Generalized PCCA), since it includes the case of non reversible processes. We also apply the method to real world eye tracking data. T3 - ZIB-Report - 18-48 KW - spectral clustering KW - Markov chain KW - Schur decomposition KW - non-reversible Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-70218 SN - 1438-0064 ER - TY - JOUR A1 - Fackeldey, Konstantin A1 - Sikorski, Alexander A1 - Weber, Marcus T1 - Spectral Clustering for Non-Reversible Markov Chains JF - Computational and Applied Mathematics N2 - Spectral clustering methods are based on solving eigenvalue problems for the identification of clusters, e.g., the identification of metastable subsets of a Markov chain. Usually, real-valued eigenvectors are mandatory for this type of algorithms. The Perron Cluster Analysis (PCCA+) is a well-known spectral clustering method of Markov chains. It is applicable for reversible Markov chains, because reversibility implies a real-valued spectrum. We also extend this spectral clustering method to non-reversible Markov chains and give some illustrative examples. The main idea is to replace the eigenvalue problem by a real-valued Schur decomposition. By this extension non-reversible Markov chains can be analyzed. Furthermore, the chains do not need to have a positive stationary distribution. In addition to metastabilities, dominant cycles and sinks can also be identified. This novel method is called GenPCCA (i.e., generalized PCCA), since it includes the case of non-reversible processes. We also apply the method to real-world eye-tracking data. KW - Spectral clustering KW - Markov chain KW - Non-reversible KW - Schur decomposition KW - GenPCCA Y1 - 2018 U6 - https://doi.org/https://doi.org/10.1007/s40314-018-0697-0 VL - 37 IS - 5 SP - 6376 EP - 6391 ER -