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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions
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to the development of Bayesian inference frameworks that use GATES. The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling frameworks to estimate
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that can estimate atmospheric trace gas source-receptor relationships, or measurement “footprints”, orders of magnitude more quickly than traditional 3D simulators (https://doi.org/10.5194/egusphere-2025
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, such as Bayesian approaches and fossilized birth–death models, to reconstruct robust phylogenies and estimate divergence times. It also investigates macroevolutionary dynamics, including variation in
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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Bayesian risk quantification for accelerated clinical development plans (C4-MPS-Oakley) School of Mathematical and Physical Sciences PhD Research Project Competition Funded Students Worldwide Prof J
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for applying proteomics and genetics data collected in situ for integrative structure modeling. Critical aspects of the research include: (1) Designing and executing methods to integrate data from different
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there are innumerable examples of its application, one important observation is the low proportion of studies proposing the estimation of uncertainties (<5%). Yet uncertainties can be multiple and of different natures
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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 6 days ago
specifically, we use simulation-based inference (SBI) [1], a Bayesian approach that leverages deep generative models, such as conditional normalizing flows and score-diffusion models, to approximate