Spatially Weighted Log-likelihoods in a Bayesian Approach to Hazard Mapping
نویسندگان
چکیده
The joint log-likelihood of a normally-distributed vector can be expressed as a linear combination of the log-likelihoods of each marginal variable in that random vector. The coefficients of this linear combination are linked to Fisher information concepts, but also to the kriging weights, when the random vector is a spatial distribution. This allows the exact estimation of the posterior or the maximum-likelihood distribution at an unsampled location conditional on the observed values and the covariance structure when the joint distribution is Gaussian, but offers also a valid approach when the marginal distributions are of any specified type.
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