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Joseph E. Lucas

  1. Efficient hierarchical clustering for continuous data.

    Пнд, 04/23/2012 - 15:01 — SomNambul
    Authors: Ricardo Henao, Joseph E. Lucas
    Subjects: Machine Learning
    Abstract

    We present an new sequential Monte Carlo sampler for coalescent based
    Bayesian hierarchical clustering. Our model is appropriate for modeling
    non-i.i.d. data and offers a substantial reduction of computational cost when
    compared to the original sampler without resorting to approximations. We also
    propose a quadratic complexity approximation that in practice shows almost no
    loss in performance compared to its counterpart.

  2. Bayesian Gaussian Copula Factor Models for Mixed Data.

    Чт, 11/03/2011 - 15:01 — SomNambul
    Authors: David B. Dunson, Lawrence Carin, Joseph E. Lucas, Jared S. Murray
    Subjects: Methodology
    Abstract

    Gaussian factor models have proven widely useful for parsimoniously
    characterizing dependence in multivariate data. There is a rich literature on
    their extension to mixed categorical and continuous variables, using latent
    Gaussian variables or through generalized latent trait models acommodating
    measurements in the exponential family. However, when generalizing to
    non-Gaussian measured variables the latent variables typically influence both
    the dependence structure and the form of the marginal distributions,
    complicating interpretation and introducing artifacts.

  3. Latent Protein Trees.

    Пт, 08/12/2011 - 15:01 — SomNambul
    Authors: Ricardo Henao, J. Will Thompson, M. Arthur Moseley, Geoffrey S. Ginsburg, Lawrence Carin, Joseph E. Lucas
    Subjects: Applications
    Abstract

    Unbiased, label-free proteomics is becoming a powerful technique for
    measuring protein expression in almost any biological sample. The output of
    these measurements after preprocessing are a collection of features (10's to
    100's of thousands) and their associated intensities for each sample. Subsets
    of features within the data are from the same peptide, subsets of peptides are
    from the same protein, and subsets of proteins are in the same biological
    pathways, therefore there is the potential for very complex and informative
    correlational structure inherent in this data.

Math-arch

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