Recent Developments in Bayesian Inference of Tokamak Plasma Equilibria and High-Dimensional Stochastic Quadratures
Gregory T. von Nessi, A M. J. Hole, The Mast Team · 2016
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 Equilib-rium 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 et. al.2012 J. Phys. A 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.