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.
Constraining formation models of binary black holes with gravitational-wave observations.The Astrophysical Journal, 846(1):82, 2017
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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.