The discussion focuses on the challenge of convoluting independent identically distributed (iid) non-central chi-square and normal distributions. The user has attempted to use characteristic functions and inverse Fourier transforms but is struggling with the latter. Suggestions include calculating the moment-generating function (MGF) by multiplying the MGFs of both distributions and using it to derive the probability density function (PDF). Alternative approaches such as term-by-term integration and Taylor series expansion are recommended for handling complex analytic distributions. The importance of re-normalizing any approximated PDF to ensure it maintains proper properties is also emphasized.