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.
Guiding diamond spin qubit growth with computational methods.Phys- ical Review Materials, 8(2):026204
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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.