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Showing 1 to 10 of 10 for “"conditional dependence"”.

  1. Conditional dependence via Shannon capacity: axioms, estimators and applications

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms

    uiuc Repository record for Conditional dependence via Shannon capacity: axioms, estimators and applications (opens in a new tab)

  2. High-Dimensional Functional Graphs and Inference for Unknown Heterogeneous Populations

    … graphical models (FGMs) to explore the conditional dependence structure among random elements. We mainly focus on the following three research projects. The first project combines the strengths of FGMs with finite mixture of regression models (FMR) to overcome the challenges of …

    vt Repository record for High-Dimensional Functional Graphs and Inference for Unknown Heterogeneous Populations (opens in a new tab)

  3. Methods and Theory for Nonparametric Inference In High-dimensional Settings

    … We propose three nonparametric measures of conditional dependence, which have theoretically optimal estimators that allow incorporation of flexible machine learning techniques and yield wald-type confidence intervals. In the second project, we propose a nonparametric parameter to measure the …

    washington Repository record for Methods and Theory for Nonparametric Inference In High-dimensional Settings (opens in a new tab)

  4. Modeling Time-Varying Networks with Applications to Neural Flow and Genetic Regulation

    … a new class of graphical models in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. We first present the model, explain how to derive it from Bayesian networks, and develop an efficient MCMC sampling algorithm that easily …

    duke Repository record for Modeling Time-Varying Networks with Applications to Neural Flow and Genetic Regulation (opens in a new tab)

  5. Bayesian causal inference of cell signal transduction from proteomics experiments

    … affect the performance of algorithms that detect conditional dependence relationships between proteins. We present a Bayesian active learning approach for designing intervention experiments that reveal the direction of causal influence between proteins. Finally, we present a Bayesian model for …

    purdue-thes Repository record for Bayesian causal inference of cell signal transduction from proteomics experiments (opens in a new tab)

  6. Three Essays on Extremes and Non-Linearities in Asset Pricing

    … market research. The first essay "Time-Varying Conditional Market Returns: Is Variance or Tail-Risk Priced", empirically investigates the question whether there is a positive relationship between aggregate market tail risk and expected returns. Based on the classical risk return trade-off, …

    passau-thes Repository record for Three Essays on Extremes and Non-Linearities in Asset Pricing (opens in a new tab)

  7. Structural exploration and inference of the network

    … Gaussian graphical models are useful to explore conditional dependence relationships between random variables through estimation of the inverse covariance matrix of a multivariate normal distribution. An estimator for such models appropriate for multiple graphs analysis in two groups is …

    njit Repository record for Structural exploration and inference of the network (opens in a new tab)

  8. The Impact of Local Item Dependence on Computer Adaptive Testing given Between and Within Testlet Adaptivity

    … performance of CAT with testlets was studied. Conditional independence is an essential assumption of Item Response Models. However, when testlets are used, item responses to the items in the same testlet may not be independent since these items are associated with a common stimulus. In this …

    umn Repository record for The Impact of Local Item Dependence on Computer Adaptive Testing given Between and Within Testlet Adaptivity (opens in a new tab)

  9. Information Processing for Biological Signals: Application to Laser Doppler Vibrometry

    … graphical models. Graphical models describe conditional dependence between random variables on a graph. When the graph is a tree, efficient algorithms exist to compute sum-marginals or max-marginals of the joint distribution. Some of the variables correspond to the measured signal, while …

    wustl Repository record for Information Processing for Biological Signals: Application to Laser Doppler Vibrometry (opens in a new tab)

  10. Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence

    … heterogeneous data with complex and nonlinear dependence are investigated in this dissertation. We apply copula theory to characterize the dependence among heterogeneous data.</p> <p>In centralized detection, where sensor observations are available at the fusion center (FC), we study …

    syracuse-diss Repository record for Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence (opens in a new tab)