Recent Developments in Bayesian Inference of Tokamak Plasma Equilibria and High-Dimensional Stochastic Quadratures

Abstract

We present recent results and technical breakthroughs for the Bayesian inference of tokamak equilibria using force-balance as a prior constraint. Issues surrounding model parameter representation and posterior analysis are discussed and addressed. These points motivate the recent advancements embodied in the Bayesian Equilibrium Analysis and Simulation Tool (BEAST) software being presently utilised to study equilibria on the Mega-Ampere Spherical Tokamak (MAST) experiment in the UK (von Nessi \emph{et. al.} 2012 \emph{J. Phys. A} \textbf{46} 185501). State-of-the-art results of using BEAST to study MAST equilbria are reviewed, with recent code advancements being systematically presented though out the manuscript.