25,882 research outputs found
Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model
Bayesian calibration of computer models tunes unknown input parameters by
comparing outputs with observations. For model outputs that are distributed
over space, this becomes computationally expensive because of the output size.
To overcome this challenge, we employ a basis representation of the model
outputs and observations: we match these decompositions to carry out the
calibration efficiently. In the second step, we incorporate the non-stationary
behaviour, in terms of spatial variations of both variance and correlations, in
the calibration. We insert two integrated nested Laplace
approximation-stochastic partial differential equation parameters into the
calibration. A synthetic example and a climate model illustration highlight the
benefits of our approach
A Riemannian-Stein Kernel Method
This paper presents a theoretical analysis of numerical integration based on
interpolation with a Stein kernel. In particular, the case of integrals with
respect to a posterior distribution supported on a general Riemannian manifold
is considered and the asymptotic convergence of the estimator in this context
is established. Our results are considerably stronger than those previously
reported, in that the optimal rate of convergence is established under a basic
Sobolev-type assumption on the integrand. The theoretical results are
empirically verified on
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