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Showing 1 to 9 of 9 for “"Concentration inequalities"”.

  1. Concentration Inequalities for Dependent Random Variables on Bayesian Networks

    The thesis presents a theoretical study of the concentration results for the function defined on the random variables on a Bayesian Network. In this work, we provide several concentration inequality results under the assumption that the function is Lipshitz or bounded difference. In addition, we …

    mit Repository record for Concentration Inequalities for Dependent Random Variables on Bayesian Networks (opens in a new tab)

  2. Applications of empirical processes in learning theory : algorithmic stability and generalization bounds

    … To analyze these properties, we focus on concentration inequalities and tools from empirical process theory. We obtain theoretical results and demonstrate their applications to machine learning. First, we show how various notions of stability upper- and lower-bound the bias and variance of …

    mit Repository record for Applications of empirical processes in learning theory : algorithmic stability and generalization bounds (opens in a new tab)

  3. Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments

    … and non-Gaussian uncertainty. We utilize concentration inequalities, higher order moments, and risk contours to handle non-Gaussian uncertainties. Without considering dynamics, we use RRT to plan trajectories together with SOS programming to verify the safety of the trajectory. Considering …

    mit Repository record for Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments (opens in a new tab)

  4. A concentration inequality based statistical methodology for inference on covariance matrices and operators

    … this manuscript, we propose using tools from the concentration of measure literature–a theory that arose in the latter half of the 20th century from connections between geometry, probability, and functional analysis–to construct rigorous descriptive and inferential statistical methodology for …

    cambridge Repository record for A concentration inequality based statistical methodology for inference on covariance matrices and operators (opens in a new tab)

  5. Magnitude, concentration, and metric complexity in phylogenetics and information theory

    … results about consistency in the classical case, concentration inequalities for independent random variables are used. However, when k << n, the dependencies between the observations cannot be ignored. So in Chapter 3, we show some McDiarmid-type concentration inequalities for leaf marginals of …

    udel Repository record for Magnitude, concentration, and metric complexity in phylogenetics and information theory (opens in a new tab)

  6. Functional inequalities in quantum information theory

    Functional inequalities constitute a very powerful toolkit in studying various problems arising in classical information theory, statistics and many-body systems. Extensions of these tools to the noncommutative setting have been introduced in the beginning of the 90's in order to study the …

    cambridge Repository record for Functional inequalities in quantum information theory (opens in a new tab)

  7. System identification for the bar model: Algorithms, consistency and sample complexity

    Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01

    uiuc Repository record for System identification for the bar model: Algorithms, consistency and sample complexity (opens in a new tab)

  8. Essays in Econometrics

    … The paper addresses this issue by providing concentration inequalities designed to detect patterns of model misspecification. The associated bounds can be used to identify subsets of individual characteristics that are not consistent with the moment restrictions. These results are applied to …

    cambridge Repository record for Essays in Econometrics (opens in a new tab)

  9. Consistency of nonparametric Bayesian methods for two statistical inverse problems arising from partial differential equations

    Partial differential equations (PDEs) govern many natural phenomena. When trying to understand the parameters driving these phenomena, we must be aware of the inevitable errors in our measurements; in statistical inverse problems these measurement errors are modelled by statistical noise. One …

    cambridge Repository record for Consistency of nonparametric Bayesian methods for two statistical inverse problems arising from partial differential equations (opens in a new tab)