The Experts below are selected from a list of 36648 Experts worldwide ranked by ideXlab platform
Luca Weihs - One of the best experts on this subject based on the ideXlab platform.
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Generic identifiability of linear structural equation models by ancestor decomposition
Scandinavian Journal of Statistics, 2016Co-Authors: Mathias Drton, Luca WeihsAbstract:Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modelling multivariate joint distributions. The models correspond to mixed graphs that include both directed and bidirected edges representing the linear relationships and correlations between noise terms, respectively. A question of interest for these models is that of Parameter identifiability, whether or not it is possible to recover edge coefficients from the joint covariance matrix of the random variables. For the problem of determining Generic Parameter identifiability, we present an algorithm building upon the half-trek criterion. Underlying our new algorithm is the idea that ancestral subsets of vertices in the graph can be used to extend the applicability of a decomposition technique.
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Generic identifiability of linear structural equation models by ancestor decomposition
arXiv: Computation, 2015Co-Authors: Mathias Drton, Luca WeihsAbstract:Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modeling multivariate joint distributions. These models correspond to mixed graphs that include both directed and bidirected edges representing the linear relationships and correlations between noise terms, respectively. A question of interest for these models is that of Parameter identifiability, whether or not it is possible to recover edge coefficients from the joint covariance matrix of the random variables. For the problem of determining Generic Parameter identifiability, we present an algorithm that extends an algorithm from prior work by Foygel, Draisma, and Drton (2012). The main idea underlying our new algorithm is the use of ancestral subsets of vertices in the graph in application of a decomposition idea of Tian (2005).
Mathias Drton - One of the best experts on this subject based on the ideXlab platform.
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Generic identifiability of linear structural equation models by ancestor decomposition
Scandinavian Journal of Statistics, 2016Co-Authors: Mathias Drton, Luca WeihsAbstract:Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modelling multivariate joint distributions. The models correspond to mixed graphs that include both directed and bidirected edges representing the linear relationships and correlations between noise terms, respectively. A question of interest for these models is that of Parameter identifiability, whether or not it is possible to recover edge coefficients from the joint covariance matrix of the random variables. For the problem of determining Generic Parameter identifiability, we present an algorithm building upon the half-trek criterion. Underlying our new algorithm is the idea that ancestral subsets of vertices in the graph can be used to extend the applicability of a decomposition technique.
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identifiability of directed gaussian graphical models with one latent source
Electronic Journal of Statistics, 2016Co-Authors: Dennis Leung, Mathias Drton, Hisayuki HaraAbstract:We study Parameter identifiability of directed Gaussian graphical models with one latent variable. In the scenario we consider, the latent vari- able is a confounder that forms a source node of the graph and is a parent to all other nodes, which correspond to the observed variables. We give a graphical condition that is sufficient for the Jacobian matrix of the parametrization map to be full rank, which entails that the parametrization is Generically finite-to- one, a fact that is sometimes also referred to as local identifiability. We also derive a graphical condition that is necessary for such identifiability. Finally, we give a condition under which Generic Parameter identifiability can be deter- mined from identifiability of a model associated with a subgraph. The power of these criteria is assessed via an exhaustive algebraic computational study on models with 4, 5, and 6 observable variables.
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Generic identifiability of linear structural equation models by ancestor decomposition
arXiv: Computation, 2015Co-Authors: Mathias Drton, Luca WeihsAbstract:Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modeling multivariate joint distributions. These models correspond to mixed graphs that include both directed and bidirected edges representing the linear relationships and correlations between noise terms, respectively. A question of interest for these models is that of Parameter identifiability, whether or not it is possible to recover edge coefficients from the joint covariance matrix of the random variables. For the problem of determining Generic Parameter identifiability, we present an algorithm that extends an algorithm from prior work by Foygel, Draisma, and Drton (2012). The main idea underlying our new algorithm is the use of ancestral subsets of vertices in the graph in application of a decomposition idea of Tian (2005).
Mona Frommert - One of the best experts on this subject based on the ideXlab platform.
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Reconstruction of signals with unknown spectra in information field theory with Parameter uncertainty
Physical Review D, 2011Co-Authors: Torsten A. Enßlin, Mona FrommertAbstract:The optimal reconstruction of cosmic metric perturbations and other signals requires knowledge of their power spectra and other Parameters. If these are not known a priori, they have to be measured simultaneously from the same data used for the signal reconstruction. We formulate the general problem of signal inference in the presence of unknown Parameters within the framework of information field theory. To solve this, we develop a Generic Parameter-uncertainty renormalized estimation (PURE) technique. As a concrete application, we address the problem of reconstructing Gaussian signals with unknown power-spectrum with five different approaches: (i) separate maximum-a-posteriori power-spectrum measurement and subsequent reconstruction, (ii) maximum-a-posteriori reconstruction with marginalized power-spectrum, (iii) maximizing the joint posterior of signal and spectrum, (iv) guessing the spectrum from the variance in the Wiener-filter map, and (v) renormalization flow analysis of the field-theoretical problem providing the PURE filter. In all cases, the reconstruction can be described or approximated as Wiener-filter operations with assumed signal spectra derived from the data according to the same recipe, but with differing coefficients. All of these filters, except the renormalized one, exhibit a perception threshold in case of a Jeffreys prior for the unknown spectrum. Data modes with variance below this threshold domore » not affect the signal reconstruction at all. Filter (iv) seems to be similar to the so-called Karhune-Loeve and Feldman-Kaiser-Peacock estimators for galaxy power spectra used in cosmology, which therefore should also exhibit a marginal perception threshold if correctly implemented. We present statistical performance tests and show that the PURE filter is superior to the others, especially if the post-Wiener-filter corrections are included or in case an additional scale-independent spectral smoothness prior can be adopted.« less
Stefanie Wuhrer - One of the best experts on this subject based on the ideXlab platform.
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analyzing clothing layer deformation statistics of 3d human motions
European Conference on Computer Vision, 2018Co-Authors: Jinlong Yang, Jeansebastien Franco, Franck Hetroywheeler, Stefanie WuhrerAbstract:Recent capture technologies and methods allow not only to retrieve 3D model sequence of moving people in clothing, but also to separate and extract the underlying body geometry and motion component and separate the clothing as a geometric layer. So far this clothing layer has only been used as raw offsets for individual applications such as retargeting a different body capture sequence with the clothing layer of another sequence, with limited scope, e.g. using identical or similar motions. The structured, semantics and motion-correlated nature of the information contained in this layer has yet to be fully understood and exploited. To this purpose we propose a comprehensive analysis of the statistics of this layer with a simple two-component model, based on PCA subspace reduction of the layer information on one hand, and a Generic Parameter regression model using neural networks on the other hand, designed to regress from any semantic Parameter whose variation is observed in a training set, to the layer Parameteriza-tion space. We show that this model not only allows to reproduce previous motion retargeting works, but generalizes the data generation capabilities of the method to other semantic Parameters such as clothing variation and size, or physical material Parameters with synthetically generated training sequence, paving the way for many kinds of capture data-driven creation and augmentation applications.
De Stefano Marco - One of the best experts on this subject based on the ideXlab platform.
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Enforcing passivity of Parameterized LTI macromodels via Hamiltonian-driven multivariate adaptive sampling
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Zanco Alessandro, Grivet-talocia Stefano, Bradde Tommaso, De Stefano MarcoAbstract:We present an algorithm for passivity verification and enforcement of multivariate macromodels whose state-space matrices depend in closed form on a set of external or design Parameters. Uniform passivity throughout the Parameter space is a fundamental requirement of Parameterized macromodels of physically passive structures, that must be guaranteed during model generation. Otherwise, numerical instabilities may occur, due to the ability of non-passive models to generate energy. In this work, we propose the first available algorithm that, starting from a Generic Parameter-depedent state-space model, identifies the regions in the frequency-Parameter space where the model behaves locally as a non-passive system. The approach we pursue is based on an adaptive sampling scheme in the Parameter space, which iteratively constructs and perturbs the eigenvalue spectrum of suitable Skew-Hamiltonian/Hamiltonian (SHH) pencils, with the objective of identifying the regions where some of these eigenvalues become purely imaginary, thus pinpointing local passivity violations. The proposed scheme is able to detect all relevant violations. An outer iterative perturbation method is then applied to the model coefficients in order to remove such violations and achieve uniform passivity. Although a formal proof of global convergence is not available, the effectiveness of the proposed implementation of the passivity verification and enforcement schemes is demonstrated on several examples