Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction
نویسندگان
چکیده
منابع مشابه
� Amap Interface for Exploring Multivariate Paleoclimate Data
We begin with an abbreviated review of recent approaches to the display of three or more geographically-referenced data variables from the literatures of statistics, computer graphics and cartography. In comparison we describe a method we have developed for exploring relationships among multivariate paleoclimate data pro duced by a global circulation model at Penn State's Earth System Science ...
متن کاملOff-Line and On-Line Fatigue Crack Growth Prediction Using Multivariate Gaussian Process
Off-line and On-line fatigue crack growth prediction of Aluminum 2024 compact-tension (CT) specimens under variable loading has been modeled, using multivariate Gaussian Process technique. The Gaussian Process model projects the input space to an output space by probabilistically inferring the underlying non-linear function relating input and output. For the off-line prediction the input space ...
متن کاملInterpretation of multivariate outliers for compositional data
data Peter Filzmoser, Karel Hron, Clemens Reimann Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstraße 8-10, A-1040 Vienna, Austria. Tel +43 1 58801 10733, FAX +43 1 58801 10799 Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Faculty of Science, 17. listopadu 12, CZ-77146 Olomouc, Czech Republic Geological...
متن کاملGaussian Process Vine Copulas for Multivariate Dependence
Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is ind...
متن کاملGaussian Process Regression for Multivariate Spectroscopic Calibration
Traditionally multivariate calibration models have been developed using regression based techniques including principal component regression and partial least squares and their non-linear counterparts. This paper proposes the application of Gaussian process regression as an alternative method for the development of a calibration model. By formulating the regression problem in a probabilistic fr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2019
ISSN: 1932-6157
DOI: 10.1214/19-aoas1281