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Showing 1 to 6 of 6 for “"f-Divergence"”.

  1. The formal definition of reference priors under a general class of divergence

    … in the contexts under Kullback-Leibler divergence. In special cases with common support and other regularity conditions, Ghosh, Mergel and Liu (2011) derived a general f-divergence criterion for prior selection. We generalize Ghosh, Mergel and Liu's (2011) results to the case without …

    missouri Repository record for The formal definition of reference priors under a general class of divergence (opens in a new tab)

  2. Derivative pricing and logarithmic portfolio optimization in incomplete markets

    … special distances but the whole class of f-divergence distances defined by strictly convex, differentiable functions. <br>Another problem studied in this thesis is the determination of optimal portfolios for logarithmic utility in general semimartingale models. The solution is given …

    freiburg-diss Repository record for Derivative pricing and logarithmic portfolio optimization in incomplete markets (opens in a new tab)

  3. Advances in Latent Variable and Causal Models

    … in each of them. First, the estimation of f- divergences is considered in a setting that is naturally satisfied in the context of autoencoders. By exploiting structural assumptions on the distributions of concern, the proposed estimator is shown to exhibit fast rates of concentration and …

    cambridge Repository record for Advances in Latent Variable and Causal Models (opens in a new tab)

  4. Automated and Provable Privatization for Black-Box Processing

    … set of information-theoretical tools based on f-divergence to characterize privacy risk through a statistical mean estimation. Provided sufficient sampling, one can approach this objective risk bound arbitrarily closely, which thus leads to a high confidence proof. The established theory also …

    mit Repository record for Automated and Provable Privatization for Black-Box Processing (opens in a new tab)

  5. Variational approximation for importance sampling and statistical inference on social influence

    Monte Carlo methods are widely used in statistical computing area to solve different problems. Social network analysis plays an importance role in many fields. In this dissertation, we focus on improving the efficiency of importance sampling, detecting the degrees of influence in networks, and …

    uiuc Repository record for Variational approximation for importance sampling and statistical inference on social influence (opens in a new tab)

  6. The fundamental limits of statistical data privacy

    The Internet is shaping our daily lives. On the one hand, social networks like Facebook and Twitter allow people to share their precious moments and opinions with virtually anyone around the world. On the other, services like Google, Netflix, and Amazon allow people to look up information, watch …

    uiuc Repository record for The fundamental limits of statistical data privacy (opens in a new tab)