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Award ID contains: 1760838

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  1. (, 10th International Workshop on Climate Informatics)
    ABSTRACT It often occurs in practice that only a small number of observations are given for reconstructing past climate events in the field of paleoclimatology. State-space models can overcome such scarcity by giving priors to those hidden states to make them correlated to one another. Inferring multiple events simultaneously from various proxies to exploit their mutual dependency is another option. Here we present a Gaussian process state-space model to reconstruct both atmospheric CO2 and sea surface temperature index from boron isotope and planktonic δ18O proxies. 
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    Full Text Available 
  2. (, 9th Annual Climate Informatics Conference proceedings)
    Abstract—To restore the historical sea surface temperatures (SSTs) better, it is important to construct a good calibration model for the associated proxies. In this paper, we introduce a new model for alkenone (UK037 ) based on the heteroscedastic Gaussian process (GP) regression method. Our nonparametric approach not only deals with the variable pattern of noises over SSTs but also contains a Bayesian method of classifying potential outliers. 
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    Accepted Manuscript Full Text Available