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pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing domain experts’ beliefs
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pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing domain experts’ beliefs
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simulations, large-scale structure evolution, weak gravitational shear, differentiable modelling, Bayesian inference or gradient-based inference methods. Experience with automatic differentiation
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos , Dr Dimitrios Roxanas Application
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-country panel providers. Causal modelling with Dynamic Bayesian Networks. Building and estimating DBNs — structure learning, temporal dependencies and causal pathways linking issue salience, framing
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researcher to develop innovative quantitative weight-of-evidence approaches based on Bayesian networks for environmental burden of disease studies. The project aims to establish a novel framework for
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must be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing
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Bayesian modelling framework to new products and dynamic storage conditions and integrating it into the MICROORC decision-support architecture; and (ii) development of data infrastructure and models
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), and ELLIS Institute Finland. You will be supervised by Dr Julien Martinelli and Associate Professor Harri Lähdesmäki. The position offers a broad local research network in Bayesian machine learning and
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Engineering, Chemistry, Materials Science, or a closely related field Knowledge of heterogeneous catalysis, reaction kinetics, thermodynamics, and structure–reactivity relationships Ability to design