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Found 4 papers, 3 papers with code
Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and $H_0$ inference
Using a fully Bayesian approach, Gaussian Process regression is extended to include marginalisation over the kernel choice and kernel hyperparameters.
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys.
Split personalities in Bayesian Neural Networks: the case for full marginalisation
The true posterior distribution of a Bayesian neural network is massively multimodal.
Constraining new physics with collider measurements of Standard Model signatures
A new method providing general consistency constraints for Beyond-the-Standard-Model (BSM) theories, using measurements at particle colliders, is presented.
High Energy Physics - Phenomenology High Energy Physics - Experiment
