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Showing 1 to 3 of 3 for “"Low-rank models"”.

  1. Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data

    … been difficult to find general constructions for models in which efficient exact inference is possible, outside of certain classical cases. We identify a class of such models that are tractable owing to a certain "low-rank" structure in the potentials that couple neighboring variables. In the …

    columbia-diss Repository record for Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data (opens in a new tab)

  2. Non-Parametric Spatial Models

    … have been developed in recent years that allow for the computation of likelihood for a very large sample size, these techniques can be applied to our non-parametric models as well. Thirdly, the most popular parametric families of covariance function are monotone--that is, the covariance …

    purdue-thes Repository record for Non-Parametric Spatial Models (opens in a new tab)

  3. Modeling, predicting, and guiding users' temporal behaviors

    … question with new exploratory and predictive models. On the other hand, these myriads of microscopic event data, such as publishing a post, forwarding a tweet, purchasing a product, checking in a place, often arise asynchronously and interdependently; hence they require new representing and …

    gatech Repository record for Modeling, predicting, and guiding users' temporal behaviors (opens in a new tab)