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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. OCBE has collaborations with leading biomedical research groups in Norway and internationally. OCBE
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wild and domestic animal populations wildlife diseases and conservation network analysis of disease spread phylodynamics model-based statistical inference using Bayesian approaches vector biology
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Virol 69, 96-100 (2015). C. Mair et al., Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. PLoS Comput Biol 15, e1007492 (2019). Option B: Create
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year round Details This research is aimed at developing scalable Bayesian approaches able to solve complex and high dimensional problems with multiple objects and multi-sensor data. One such problem is
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tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations. Therefore, the objectives of this research
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challenging data problem. Weak signals from collisions of compact objects can be dug out of noisy time series because we understand what the signal should look like, and can therefore use simple algorithms
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Expertise in quantitative modeling, computational and/or Bayesian methods Expertise using at least one programming languages in the analysis of scientific data such as R, Python, Matlab, or Julia. Expertise