Interpolating between small- and large- g expansions using Bayesian model mixing

  title={Interpolating between small- and large-
 expansions using Bayesian model mixing},
  author={Alexandra Semposki and Richard J. Furnstahl and Daniel R. Phillips},
  journal={Physical Review C},
Bayesian Model Mixing (BMM) is a statistical technique that can be used to combine models that are predictive in different input domains into a composite distribution that has improved predictive power over the entire input space. We explore the application of BMM to the mixing of two expansions of a function of a coupling constant g that are valid at small and large values of g respectively. This type of problem is quite common in nuclear physics, where physical properties are… 



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