Sigrunn Holbek Sørbye

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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)
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)
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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