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    ISPD
    2006
    ACM
    79views Hardware» more  ISPD 2006»
    Non-gaussian statistical parameter modeling for SSTA with confidence interval analysis
    14 years 1 months ago
    Non-gaussian statistical parameter modeling for SSTA with confidence interval analysis
    Download  cc.ee.ntu.edu.tw
    Lizheng Zhang, Jun Shao, Charlie Chung-Ping Chen
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    BMCBI
    2010
    113views more  BMCBI 2010»
    Probabilistic Principal Component Analysis for Metabolomic Data
    13 years 7 months ago
    Probabilistic Principal Component Analysis for Metabolomic Data
    Download  www.biomedcentral.com
    Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique fo...
    Gift Nyamundanda, Lorraine Brennan, Isobel Claire ...
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    WSC
    2004
    124views Modeling And Simulation» more  WSC 2004»
    A Data Warehouse Environment for Storing and Analyzing Simulation Output Data
    13 years 8 months ago
    A Data Warehouse Environment for Storing and Analyzing Simulation Output Data
    Download  www.informs-sim.org
    Discrete event simulation modelling has been extensively used in modelling complex systems. Although it offers great conceptual-modelling flexibility, it is both computationally e...
    Christos Vasilakis, Elia El-Darzi, Panagiotis Chou...
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    WSC
    1997
    178views Modeling And Simulation» more  WSC 1997»
    AutoStat: Output Statistical Analysis for AutoMod Users
    13 years 8 months ago
    AutoStat: Output Statistical Analysis for AutoMod Users
    Download  www.informs-sim.org
    AutoStat™ is an extension package for AutoMod™ and AutoSched™ models that provides complete support for simulation model experimentation and statistical analysis of outputs....
    John S. Carson II
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    PRL
    2000
    182views more  PRL 2000»
    Bayesian MLP neural networks for image analysis
    13 years 7 months ago
    Bayesian MLP neural networks for image analysis
    Download  www.lce.hut.fi
    We demonstrate the advantages of using Bayesian multi layer perceptron (MLP) neural networks for image analysis. The Bayesian approach provides consistent way to do inference by c...
    Aki Vehtari, Jouko Lampinen
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