  In this paper, current profiles for plasma discharges on the Mega-Ampere
Spherical Tokamak (MAST) are directly calculated from pickup coil, flux loop
and Motional-Stark Effect (MSE) observations via methods based in the
statistical theory of Bayesian analysis. By representing toroidal plasma
current as a series of axisymmetric current beams with rectangular
cross-section and inferring the current for each one of these beams,
flux-surface geometry and q-profiles are subsequently calculated by elementary
application of Biot-Savart's law. The use of this plasma model in the context
of Bayesian analysis was pioneered by Svensson and Werner on the Joint-European
Tokamak (JET) [J. Svensson and A. Werner. Current tomography for axisymmetric
plasmas. {\em Plasma Physics and Controlled Fusion}, 50(8):085002, 2008]. In
this framework, linear forward models are used to generate diagnostic
predictions, and the probability distribution for the currents in the
collection of plasma beams was subsequently calculated directly via application
of Bayes' formula. In this work, we introduce a new diagnostic technique to
identify and remove outlier observations associated with diagnostics falling
out of calibration or suffering from an unidentified malfunction. These
modifications enable good agreement between Bayesian inference of the last
closed flux-surface (LCFS) with other corroborating data, such as such as that
from force balance considerations using EFIT++ [L. Appel et al., Proc. 33rd EPS
Conf., Rome, Italy, 2006]. In addition, this analysis also yields errors on the
plasma current profile and flux-surface geometry, as well as directly
predicting the Shafranov shift of the plasma core.
