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

@article{Semposki2022InterpolatingBS,
  title={Interpolating between small- and large-
g
 expansions using Bayesian model mixing},
  author={Alexandra Semposki and Richard J. Furnstahl and Daniel R. Phillips},
  journal={Physical Review C},
  year={2022}
}
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… 

References

SHOWING 1-10 OF 24 REFERENCES

Constrained Extrapolation Problem and Order‐Dependent Mappings

The problem of extrapolating the perturbation series for the dilute Fermi gas in three dimensions to the unitary limit of infinite scattering length and into the BEC region is considered, using the

Statistical aspects of nuclear mass models

This work investigates the structure of the 4-parameter Liquid Drop Model by considering discrepant mass domains for calibration, and uses the chi-square correlation framework to analyze the 14- Parameter Skyrme energy density functional calibrated using homogeneous and heterogeneous datasets.

Modelling spatially correlated data via mixtures: a Bayesian approach

Summary. The paper develops mixture models for spatially indexed data. We confine attention to the case of finite, typically irregular, patterns of points or regions with prescribed spatial

emcee: The MCMC Hammer

This document introduces a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare and describes the algorithm and the details of the implementation.

Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics

We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and

Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors

Bayesian model averaging (BMA) provides a coherent mechanism for ac- counting for this model uncertainty and provides improved out-of- sample predictive performance.

Numerical Approximation to the Thermodynamic Integrals

We approximate boson thermodynamic integrals as polynomials in two variables chosen to give the correct limiting expansion and to smoothly interpolate into other regimes. With 10 free parameters, an

Bayesian Additive Regression Trees: A Review and Look Forward

The basic approach to BART is presented and further development of the original algorithm that supports a variety of data structures and assumptions are discussed, including augmentations of the prior specification to accommodate higher dimensional data and smoother functions.

Measurement of the proton spin structure at long distances

Measuring the spin structure of protons and neutrons tests our understanding of how they arise from quarks and gluons, the fundamental building blocks of nuclear matter. At long distances, the

Bayesian Analysis

After making some general remarks, I consider two examples that illustrate the use of Bayesian Probability Theory. The first is a simple one, the physicist’s favorite “toy,” that provides a forum for