Hunting for bumps in the margins
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Instrumentation"
DOI: 10.1088/1748-0221/18/05/p05014
Abstract: Data driven modelling is vital to many analyses at collider experiments, however the derived inference of physical properties becomes subject to details of the model fitting procedure. This work brings a principled Bayesian picture —… read more here.
Keywords: physics; hunting bumps; marginal likelihood; bumps margins ... See more keywords
Consensus Based Distributed Sparse Bayesian Learning by Fast Marginal Likelihood Maximization
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2020.3039481
Abstract: For swarm systems, distributed processing is of paramount importance and Bayesian methods are preferred for their robustness. Existing distributed sparse Bayesian learning (SBL) methods rely on the automatic relevance determination (ARD), which involves a computationally… read more here.
Keywords: fast marginal; distributed sparse; likelihood maximization; marginal likelihood ... See more keywords
Embedded Feature Selection Based on Relevance Vector Machines With an Approximated Marginal Likelihood and its Industrial Application
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2021.3049597
Abstract: Feature selection is of great importance to make prediction for process variables in industrial production. An embedded feature selection method, based on relevance vector machines with an approximated marginal likelihood function, is proposed in this… read more here.
Keywords: selection; relevance; marginal likelihood; feature selection ... See more keywords