In recent years, Bayesian probability theory has been used in a number of experiments to fold uncertainties and interdependences in the diagnostic data and forward models, and together with prior knowledge of the state of the plasma, thus increase accuracy of inferred physics variables. Key developments include the application to current and flux surface tomography, effective charge, the electron energy distribution function, neutron spectrometry and density. Virtual observations have also been introduced to better constrain inferred quantities in current tomography. In this work we present Bayesian inference results of toroidal and poloidal current and flux surface tomography. Whilst the uncertainty in these profiles, as well as the uncertainty in inferred parameters such as the safety factor profile is small (<5%), the inference can change substantially depending on the physics model used. We also present Bayesian inference results for Thomson scattering and charge-exchange recombination spectroscopy. In separate work we have computed radial force balance components on the midplane in the Mega Ampére Spherical tokamak. Our aim is to establish a validation framework for different equilibrium physics models. We find that in the overlapping region of the core (normalized poloidal flux less than 0.4) and motional Stark effect (MSE) chords, the plasma is consistent with static Grad–Shafranov force balance to within two standard deviations. In the outboard edge region, where MSE data are also available, the pressure gradient exceeds the Lorentz force. Most likely, this is because the poloidal current is not constrained to zero at the plasma edge. To lowest order, the results suggest computing components of force balance are useful to assess data-consistency, independent of any equilibrium solution. To first order, we have integrated the residue to force balance to infer an energetic particle pressure.