  A Bayesian with GP(Gaussian Process)-based numerical method to impute a few
missing magnetic signals caused by impaired magnetic probes during tokamak
operations is developed such that the real-time reconstruction of magnetic
equilibria, whose performance strongly depends on the measured magnetic signals
and their intactness, are affected minimally. Likelihood of the Bayesian model
constructed with the Maxwell's equations, specifically Gauss's law of magnetism
and Amp\`ere's law, results in infinite number of solutions if two or more
magnetic signals are missing. This undesirable characteristic of the Bayesian
model is remediated by coupling the model with the Gaussian process. Our
proposed numerical method infers the missing magnetic signals correctly in less
than $1$\:msec suitable for real-time reconstruction of magnetic equilibria
during tokamak operations. The method can also be used for a neural network
that reconstructs magnetic equilibria trained with a complete set of magnetic
signals. Without our proposed imputation method, such a neural network would
become useless if missing signals are not tolerable by the network.
