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Spatial Statistics III Stat 518 Sp08
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Bayesian kriging Instead of estimating the parameters, we put a prior distribution on them, and update the distribution using the data. Model: Prior: Posterior:
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geoR Prior is assigned to and. The latter assumed zero unless specified. The distributions are discretized. Default prior on mean is flat (if not specified, assumed constant). (Lots of different assignments are possible)
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Prior/posterior of
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Variogram estimates mean median WLS
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Bayes vs universal kriging Bayes predictive meanUniversal kriging
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Spectral representation By the spectral representation any isotropic continuous correlation on R d is of the form By isotropy, the expectation depends only on the distribution G of. Let Y be uniform on the unit sphere. Then
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Isotropic correlation J v (u) is a Bessel function of the first kind and order v. Hence and in the case d=2 (Hankel transform)
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The Bessel function J 0 –0.403
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The exponential correlation A commonly used correlation function is (v) = e –v/ . Corresponds to a Gaussian process with continuous but not differentiable sample paths. More generally, (v) = c(v=0) + (1-c)e –v/ has a nugget c, corresponding to measurement error and spatial correlation at small distances. All isotropic correlations are a mixture of a nugget and a continuous isotropic correlation.
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The squared exponential Usingyields corresponding to an underlying Gaussian field with analytic paths. This is sometimes called the Gaussian covariance, for no really good reason. A generalization is the power(ed) exponential correlation function,
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The spherical Corresponding variogram nugget sillrange
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The Matérn class where is a modified Bessel function of the third kind and order . It corresponds to a spatial field with –1 continuous derivatives =1/2 is exponential; =1 is Whittle’s spatial correlation; yields squared exponential.
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Some other covariance/variogram families NameCovarianceVariogram Wave Rational quadratic LinearNone Power lawNone
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