Bayesian model mixing (BMM) is a statistical technique that can combine constraints from different regions of an input space in a principled way. Here we extend our BMM framework for the equation of state (EOS) of strongly interacting matter from symmetric nuclear matter to asymmetric matter, specifically focusing on zero-temperature, charge-neutral, $\beta$-equilibrated matter. We use Gaussian processes (GPs) to infer constraints on the neutron-star matter EOS at intermediate densities from two different microscopic theories: chiral effective-field theory ($\chi \mathrm{EFT}$) at baryon densities around nuclear saturation, $n_B\sim n_0$, and perturbative QCD at asymptotically high baryon densities, $n_B⩾20n_0$. The uncertainties of the $\chi \mathrm{EFT}$ and pQCD EOSs are obtained using the BUQEYE truncation error model. We demonstrate the flexibility of our framework through the use of two categories of GP kernels: conventional stationary kernels and a nonstationary changepoint kernel. We use the latter to explore potential constraints on the dense matter EOS by including exogenous data representing theory predictions and heavy-ion collision measurements at densities $⩾2n_0$. We also use our EOSs to obtain neutron-star mass-radius relations and their uncertainties. Our framework, whose implementation will be available through a GitHub repository, provides a prior distribution for the EOS that can be used in large-scale neutron-star inference frameworks.

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• Asymmetric nuclear matter
• Effective field theory
• Equations of state of nuclear matter
• Nuclear astrophysics
• Perturbative QCD
• Quark matter
• Neutron stars & pulsars
• Bayesian methods
• Statistical methods