Sigrunn Holbek Sørbye

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We propose a method for restoring the underlying true signal in noisy functional images. The Nadaraya-Watson (NW) estimator described in, e.g., [1] is a classical nonparametric estimator for this problem. Since the true scene in many applications contains abrupt changes between pixels of different types, a modification of the NW estimator is needed. In the(More)
In this paper we introduce a new method for performing computational inference on log-Gaussian Cox processes (LGCP). Contrary to current practice, we do not approximate by a counting process on a partition of the domain, but rather attack the point process likelihood directly. In order to do this, we use the continuously specified Markovian random fields(More)
Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses how a joint log Gaussian Cox process model may be fitted to independent replicated point patterns. We(More)
In recent years, disease mapping studies have become a routine application within geographical epidemiology and are typically analysed within a Bayesian hierarchical model formulation. A variety of model formulations for the latent level have been proposed but all come with inherent issues. In the classical BYM (Besag, York and Mollié) model, the spatially(More)
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