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Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling gener...
This paper introduces a spatio-temporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatio-temporal data. The model is derived as a sp...
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
We study the problem of incorporating covariates in a compound decision setup. It is desired to estimate the means of $n$ response variables, which are independent and normally distributed, and each i...
This paper illustrates a novel hierarchical dynamic Bayesian network modelling the spiking patterns of neuronal ensembles over time. We introduce, at separate model stages, the parameters characteri...

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