  The first step to realize automatic experimental data analysis for fusion
plasma experiments is fitting noisy data of temperature and density spatial
profiles, which are obtained routinely. However, it has been difficult to
construct algorithms that fit all the data without over- and under-fitting. In
this paper, we show that this difficulty originates from the lack of knowledge
of the probability distribution that the measurement data follow. We
demonstrate the use of a machine learning technique to estimate the data
distribution and to construct an optimal generative model. We show that the
fitting algorithm based on the generative modeling outperforms classical
heuristic methods in terms of the stability as well as the accuracy.
