A hybridized MCMC framework performs trans-dimensional model selection and parameter estimation from sparse noisy data, recovering nuclear spin locations and couplings around spin defects with an order of magnitude less data than existing approaches.
Generalizing bayesian phylogenetics to infer shared evolutionary events.Proceedings of the National Academy of Sciences, 119(29):e2121036119, 2022
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Trans-dimensional Hamiltonian model selection and parameter estimation from sparse, noisy data
A hybridized MCMC framework performs trans-dimensional model selection and parameter estimation from sparse noisy data, recovering nuclear spin locations and couplings around spin defects with an order of magnitude less data than existing approaches.