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) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts of complex models. BioM is an
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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for learning about models from data, 2) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts
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@astro.uio.no ) and Prof. Hans Kristian Eriksen (h.k.k.eriksen@astro.uio.no ). The main goal of this position is to implement a novel Bayesian re-analysis pipeline for Planck HFI in the Commander pipeline, and