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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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at Stockholm University. We have a strong tradition in sampling but areas that we are growing in include, but are not limited to, Bayesian inference, the intersection of statistics and machine learning
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projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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international projects, including: The post-holder will run numerical models that simulate the dispersion of greenhouse gases through the atmosphere. These models will be used, in Bayesian inference frameworks
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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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background in one of the following areas: Statistical Physics Applied Mathematics Statistics & Bayesian Inference Proficiency in Python is also expected. Contacts dbc-epi-recrutement at pasteur dot fr
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testing, propensity score methods, meta-analysis, Bayesian inference, and a wide range of regression models (linear, logistic, Poisson, negative binomial, lognormal, Cox, mixed-effects, GEE, penalized
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in knowledge-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov
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revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit priors on the latent variables. Having a clear
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selection criterion in some extent. This strongly suggests revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit